Navlab autonomous cars 1 through 5. NavLab 1 (farthest in photo) was started in 1984 and completed in 1986. Navlab 5 (closest vehicle), finished in 1995, was the first car to drive coast-to-coast in the United States autonomously.

An autonomous car (also known as a driverless car, self-driving car, robotic car, autos ) and is a that is capable of sensing its environment and navigating without. Autonomous cars use a variety of techniques to detect their surroundings, such as,,, and. Advanced interpret to identify appropriate navigation paths, as well as obstacles and relevant.

Autonomous cars must have control systems that are capable of analyzing sensory data to distinguish between different cars on the road. The potential benefits of autonomous cars include reduced mobility and infrastructure costs, increased safety, increased mobility, increased customer satisfaction and reduced crime.

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Specifically a significant reduction in; the resulting injuries; and related costs, including less need for. Autonomous cars are predicted to increase traffic flow; provided enhanced mobility for children, the, and the poor; relieve travelers from driving and navigation chores; lower fuel consumption; significantly reduce needs for; reduce crime; and facilitate business models for, especially via the. On the downside, a frequently cited paper by Michael Osborne and found that autonomous cars would render many jobs redundant. Among the main obstacles to widespread adoption are technological challenges, disputes concerning liability; the time period needed to replace the existing stock of vehicles; resistance by individuals to forfeit control; consumer safety concerns; implementation of a workable and establishment of; risk of loss of privacy and security concerns, such as hackers or terrorism; concerns about the resulting loss of driving-related jobs in the road; and risk of increased as travel becomes less costly and time-consuming. Many of these issues are due to the fact that, for the first time, allow computers to roam freely, with many related and security concerns.

The 's modified 1960 to be automatically controlled at the. Experiments have been conducted on automating driving since at least the 1920s; promising trials took place in the 1950s. The first truly autonomous prototype cars appeared in the 1980s, with 's and ALV projects in 1984 and and 's in 1987.

Since then, numerous companies and research organizations have developed prototypes. In 2015, the US states of,,,, and, together with allowed the testing of autonomous cars on public roads. In 2017 Audi stated that its latest would be autonomous at up to speeds of 60 km/h using its 'Audi AI'.

The driver would not have to do safety checks such as frequently gripping the steering wheel. The Audi A8 was claimed to be the first production car to reach level 3 autonomous driving and Audi would be the first manufacturer to use laser scanners in addition to cameras and ultrasonic sensors for their system. On the 7th November 2017, announced that it had begun testing driverless cars without a safety driver at the driver position, however; there is still an employee in the car. Autonomous vs. Automated [ ] Autonomous means self-governance. Many historical projects related to vehicle autonomy have been automated (made to be automatic) due to a heavy reliance on artificial hints in their environment, such as magnetic strips. Autonomous control implies satisfactory performance under significant uncertainties in the environment and the ability to compensate for system failures without external intervention.

One approach is to implement both in the immediate vicinity (for ) and further away (for congestion management). Such outside influences in the decision process reduce an individual vehicle's autonomy, while still not requiring human intervention. (2012) write 'This Article generally uses the term 'autonomous,' instead of the term 'automated.' ' The term 'autonomous' was chosen 'because it is the term that is currently in more widespread use (and thus is more familiar to the general public). However, the latter term is arguably more accurate. 'Automated' connotes control or operation by a machine, while 'autonomous' connotes acting alone or independently.

Most of the vehicle concepts (that we are currently aware of) have a person in the driver’s seat, utilize a communication connection to the Cloud or other vehicles, and do not independently select either destinations or routes for reaching them. Thus, the term 'automated' would more accurately describe these vehicle concepts'. As of 2017, most commercial projects focused on autonomous vehicles that did not communicate with other vehicles or an enveloping management regime. Classification [ ]. The aim of the Volvo Drive Me project, which is using test vehicles, is to develop SAE level 4 cars.

According to CNET journalist Tim Stevens, the Drive Me autonomous test vehicle is considered ”Level 3 autonomous driving”, apparently referring to the now defunct NHTSA classification system levels. A classification system based on six different levels (ranging from fully manual to fully automated systems) was published in 2014 by, an automotive standardization body, as J3016, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. This classification system is based on the amount of driver intervention and attentiveness required, rather than the vehicle capabilities, although these are very loosely related. In the United States in 2013, the (NHTSA) released a formal classification system, but abandoned this system in favor of the SAE standard in 2016. Also in 2016, SAE updated its classification, called J3016_201609.

Levels of driving automation [ ] In SAE's autonomy level definitions, 'driving mode' means 'a type of driving scenario with characteristic dynamic driving task requirements (e.g., expressway merging, high speed cruising, low speed traffic jam, closed-campus operations, etc.)' • Level 0: Automated system issues warnings may momentarily intervene but has no sustained vehicle control. • Level 1 (”hands on”): Driver and automated system shares control over the vehicle. An example would be Adaptive Cruise Control (ACC) where the driver controls steering and the automated system controls speed. Using Parking Assistance, steering is automated while speed is manual. The driver must be ready to retake full control at any time. Lane Keeping Assistance (LKA) Type II is a further example of level 1 self driving.

• Level 2 (”hands off”): The automated system takes full control of the vehicle (accelerating, braking, and steering). The driver must monitor the driving and be prepared to immediately intervene at any time if the automated system fails to respond properly. The shorthand ”hands off” is not meant to be taken literally. In fact, contact between hand and wheel is often mandatory during SAE 2 driving, to confirm that the driver is ready to intervene. • Level 3 (”eyes off”): The driver can safely turn their attention away from the driving tasks, e.g. The driver can text or watch a movie. The vehicle will handle situations that call for an immediate response, like emergency braking.

The driver must still be prepared to intervene within some limited time, specified by the manufacturer, when called upon by the vehicle to do so. In 2017 the Audi A8 Luxury Sedan was the first commercial car to claim to be able to do level 3 self driving. The car has a so-called Traffic Jam Pilot. When activated by the human driver the car takes full control of all aspects of driving in slow-moving traffic at up to 60 kilometers per hour. The function only works on highways with a physical barrier separating oncoming traffic. • Level 4 (”mind off”): As level 3, but no driver attention is ever required for safety, i.e.

The driver may safely go to sleep or leave the driver's seat. Self driving is supported only in limited areas () or under special circumstances, like traffic jams. Outside of these areas or circumstances, the vehicle must be able to safely abort the trip, i.e. Park the car, if the driver does not retake control. • Level 5 (”steering wheel optional”): No human intervention is required.

An example would be a robotic taxi. In the formal SAE definition below, note in particular what happens in the shift from SAE 2 to from SAE 3: the human driver no longer has to monitor the environment. This is the final aspect of the ”dynamic driving task” that is now passed over from the human to the automated system. At SAE 3, the human driver still has the responsibility to intervene when asked to do this by the automated system.

At SAE 4 the human driver is relieved of that responsibility and at SAE 5 the automated system will never need to ask for an intervention. Main article: Modern self-driving cars generally use Bayesian (SLAM) algorithms, which fuse data from multiple sensors and an off-line map into current location estimates and map updates. SLAM with detection and tracking of other moving objects (DATMO), which also handles things such as cars and pedestrians, is a variant being developed at Google. Simpler systems may use roadside (RTLS) beacon systems to aid localisation. Typical sensors include,, and. Visual object recognition uses including. Is developing an open-source software stack.

Autonomous cars are being developed with deep learning, or neural networks. Deep neural networks have many computational stages, or levels in which neurons are simulated from the environment that activate the network. The neural network depends on an extensive amount of data extracted from real life driving scenarios. The neural network is activated and “learns” to perform the best course of action. Deep learning has been applied to answer to real life situations, and is used in the programming for autonomous cars. In addition, sensors, such as the LIDAR sensors already used in self-driving cars; cameras to detect the environment, and precise GPS navigation will be used in autonomous cars. Testing [ ] Testing vehicles with varying degrees of autonomy can be done physically, in closed environments, on public roads (where permitted, typically with a license or permit or adhering to a specific set of operating principles ) or virtually, i.e.

In computer simulations. When driven on public roads, autonomous vehicles require a person to monitor their proper operation and 'take over' when needed. Autonomous trucks [ ] Several companies are said to be testing autonomous technology in semi trucks., a self-driving trucking company that was acquired by Uber in August 2016, demoed their trucks on the highway before being acquired. In May 2017, San Francisco-based startup Embark announced a partnership with truck manufacturer to test and deploy autonomous technology in Peterbilt's vehicles. Google's has also said to be testing autonomous technology in trucks, however no timeline has been given for the project.

Transport systems [ ] In Europe, cities in Belgium, France, Italy and the UK are planning to operate transport systems for autonomous cars, and Germany, the Netherlands, and Spain have allowed public testing in traffic. In 2015, the UK launched public trials of the autonomous pod in.

Beginning in summer 2015 the French government allowed to make trials in real conditions in the Paris area. The experiments were planned to be extended to other cities such as Bordeaux and Strasbourg by 2016. The alliance between French companies and (provider of the first self-parking car system that equips Audi and Mercedes premi) is testing its own system. New Zealand is planning to use autonomous vehicles for public transport in Tauranga and Christchurch.

Potential advantages [ ] Safety [ ] (and and costs), caused by human errors, such as delayed,,, and other forms of or should be substantially reduced. Consulting firm estimated that widespread use of autonomous vehicles could 'eliminate 90% of all auto accidents in the United States, prevent up to US$190 billion in damages and health-costs annually and save thousands of lives.' Welfare [ ] Autonomous cars could reduce; relieve travelers from driving and navigation chores, thereby replacing behind-the-wheel commuting hours with more time for leisure or work; and also would lift constraints on occupant ability to drive, and, intoxicated, prone to, or otherwise impaired. For the young, the,, and low-income citizens, autonomous cars could provide enhanced. The removal of the steering wheel—along with the remaining driver interface and the requirement for any occupant to assume a forward-facing position—would give the interior of the cabin greater ergonomic flexibility. Large vehicles, such as motorhomes, would attain appreciably enhanced ease of use. Traffic [ ] Additional advantages could include higher; smoother rides; and increased roadway capacity; and minimized, due to decreased need for safety gaps and higher speeds.

Currently, maximum throughput or capacity according to the U.S. Is about 2,200 passenger vehicles per hour per lane, with about 5% of the available road space is taken up by cars. One study estimated that autonomous cars could increase capacity by 273% (~8,200 cars per hour per lane). The study also estimated that with 100% connected vehicles using vehicle-to-vehicle communication, capacity could reach 12,000 passenger vehicles per hour (up 445% from 2,200 pc/h per lane) traveling safely at 120 km/h (75 mph) with a following gap of about 6 m (20 ft) of each other. Currently, at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away from the car in front.

These increases in highway capacity could have a significant impact in traffic congestion, particularly in urban areas, and even effectively end highway congestion in some places. The ability for authorities to manage would increase, given the extra data and driving behavior predictability. Combined with less need for and even. Costs [ ] Safer driving was expected to reduce the costs of. Reduced traffic congestion and the improvements in traffic flow due to widespread use of autonomous cars will also translate into better. Related effects [ ] By reducing the (labor and other) cost of, autonomous cars could reduce the number of cars that are individually owned, replaced by taxi/pooling and other car sharing services.

This could dramatically reduce the need for, freeing scarce land for other uses.This would also dramatically reduce the size of the automotive production industry, with corresponding environmental and economic effects. Assuming the increased efficiency is not fully offset by increases in demand, more efficient traffic flow could free roadway space for other uses such as better support for pedestrians and cyclists. The vehicles' increased awareness could aid the police by reporting on illegal passenger behavior, while possibly enabling other crimes, such as deliberately crashing into another vehicle or a pedestrian. Potential obstacles [ ]. This section is in a list format that may be better presented using. You can help by converting this section to prose, if. Is available.

(December 2016) In spite of the various benefits to increased vehicle automation, some foreseeable challenges persist, such as disputes concerning liability, the time needed to turn the existing stock of vehicles from nonautonomous to autonomous, resistance by individuals to forfeit control of their cars, customer concern about the safety of driverless cars, and the implementation of legal framework and establishment of government regulations for self-driving cars. Other obstacles could be missing driver experience in potentially dangerous situations, ethical problems in situations where an autonomous car's software is forced during an unavoidable crash to choose between multiple harmful courses of action, and possibly insufficient Adaptation to Gestures and non-verbal cues by police and pedestrians. Possible technological obstacles for autonomous cars are: • Software reliability.

• Artificial Intelligence still isn't able to function properly in chaotic inner city environments • A car's computer could potentially be compromised, as could a communication system between cars. • Susceptibility of the car's sensing and navigation systems to different types of weather or deliberate interference, including jamming and spoofing. • Avoidance of large animals requires recognition and tracking, and Volvo found that software suited to,, and was ineffective with.

• Autonomous cars may require very high-quality specialised maps to operate properly. Where these maps may be out of date, they would need to be able to fall back to reasonable behaviors. • Competition for the radio spectrum desired for the car's communication. • Field programmability for the systems will require careful evaluation of product development and the component supply chain. • Current road infrastructure may need changes for autonomous cars to function optimally. • Cost (purchase, maintenance, repair and insurance) of autonomous vehicle as well as total cost of infrastructure spending to enable autonomous vehicles and the cost sharing model. • Discrepancy between people’s beliefs of the necessary government intervention may cause a delay in accepting autonomous cars on the road.

Whether the public desires no change in existing laws, federal regulation, or another solution; the framework of regulation will likely result in differences of opinion. Potential disadvantages [ ].

See also: A direct impact of widespread adoption of autonomous vehicles is the loss of driving-related jobs in the road transport industry. There could be resistance from professional drivers and unions who are threatened by job losses. In addition, there could be job losses in public transit services and crash repair shops. The automobile insurance industry might suffer as the technology makes certain aspects of these occupations obsolete. Privacy could be an issue when having the vehicle's location and position integrated into an interface in which other people have access to. In addition, there is the risk of through the sharing of information through (Vehicle to Vehicle) and (Vehicle to Infrastructure) protocols.

There is also the risk of terrorist attacks. Self-driving cars could potentially be loaded with explosives and used as. The lack of stressful driving, more productive time during the trip, and the potential savings in travel time and cost could become an incentive to live far away from cities, where land is cheaper, and work in the city's core, thus increasing travel distances and inducing more, more fuel consumption and an increase in the of urban travel.

There is also the risk that traffic congestion might increase, rather than decrease. Appropriate public policies and regulations, such as zoning, pricing, and urban design are required to avoid the negative impacts of increased suburbanization and longer distance travel. Some believe that once automation in vehicles reaches higher levels and becomes reliable, drivers will pay less attention to the road. Research shows that drivers in autonomous cars react later when they have to intervene in a critical situation, compared to if they were driving manually. Ethical and moral reasoning come into consideration when programming the software that decides what action the car takes in an unavoidable crash; whether the autonomous car will crash into a bus, potentially killing people inside; or swerve elsewhere, potentially killing its own passengers or nearby pedestrians. A question that comes into play that programmers find difficult to answer is “what decision should the car make that causes the ‘smallest’ damage when it comes to people’s lives?” The ethics of autonomous vehicles is still in the process of being solved and could possibly lead to controversiality.

Safety record [ ] Mercedes autonomous cruise control system [ ] In 1999, introduced Distronic, the first -assisted, on the and the. The Distronic system was able to adjust the vehicle speed automatically to the car in front in order to always maintain a safe distance to other cars on the road.

The forward-facing Distronic sensors are usually placed behind the Mercedes-Benz logo and front grille. In 2005, Mercedes refined the system (from this point called ' Distronic Plus') with the being the first car to receive the upgraded Distronic Plus system. Distronic Plus could now completely halt the car if necessary on E-Class and most Mercedes sedans.

In an episode of, demonstrated the effectiveness of the cruise control system in the S-class by coming to a complete halt from motorway speeds to a round-about and getting out, without touching the pedals. By 2017, Mercedes has vastly expanded its autonomous driving features on production cars: In addition to the standard Distronic Plus features such as an active brake assist, Mercedes now includes a steering pilot, a parking pilot, a cross-traffic assist system, night-vision cameras with automated danger warnings and braking assist (in case animals or pedestrians are on the road for example), and various other autonomous-driving features. In 2016, Mercedes also introduced its Active Brake Assist 4, which was the first emergency braking assistant with pedestrian recognition on the market.

Due to Mercedes' history of gradually implementing advancements of their autonomous driving features that have been extensively tested, not many crashes that have been caused by it are known. One of the known crashes dates back to 2005, when German ' ' was testing Mercedes' old Distronic system. During the test, the system did not always manage to brake in time. Ulrich Mellinghoff, then Head of Safety, NVH, and Testing at the Mercedes-Benz Technology Centre, stated that some of the tests failed due to the vehicle being tested in a metallic hall, which caused problems with the system's radar. Later iterations of the Distronic system have an upgraded radar and numerous other sensors, which are not susceptible to a metallic environment anymore.

In 2008, Mercedes conducted a study comparing the crash rates of their vehicles equipped with Distronic Plus and the vehicles without it, and concluded that those equipped with Distronic Plus have an around 20% lower crash rate. In 2013, German driver was invited by Mercedes to try to crash a vehicle, which was equipped with all safety features that Mercedes offered for its production vehicles at the time, which included the Active Blind Spot Assist, Active Lane Keeping Assist, Brake Assist Plus, Collision Prevention Assist, Distronic Plus with Steering Assist, Pre-Safe Brake, and Stop&Go Pilot. Due to the safety features, Schumacher was unable to crash the vehicle in realistic scenarios.

Tesla Autopilot [ ]. Main article: In mid‑October 2015 rolled out version 7 of their software in the U.S. That included capability. On 9 January 2016, Tesla rolled out version 7.1 as an update, adding a new 'summon' feature that allows cars to self-park at parking locations without the driver in the car. Tesla's autonomous driving features can be classified as somewhere between level 2 and level 3 under the ’s (NHTSA) five levels of vehicle automation.

At this level the car can act autonomously but requires the full attention of the driver, who must be prepared to take control at a moment's notice. Autopilot should be used only on, and sometimes it will fail to detect lane markings and disengage itself.

In urban driving the system will not read traffic signals or obey stop signs. The system also does not detect pedestrians or cyclists. In use in July 2016 was suitable only on not for urban driving. Among other limitations, it could not detect pedestrians or cyclists. The first fatal accident involving a vehicle being driven by itself took place in on 7 May 2016 while a was engaged in Autopilot mode. The occupant was killed in a crash with an 18-wheel.

On 28 June 2016 the National Highway Traffic Safety Administration (NHTSA) opened a formal investigation into the accident working with the. According to the NHTSA, preliminary reports indicate the crash occurred when the tractor-trailer made a left turn in front of the Tesla at an intersection on a non-controlled access highway, and the car failed to apply the brakes.

The car continued to travel after passing under the truck’s trailer. The NHTSA's preliminary evaluation was opened to examine the design and performance of any automated driving systems in use at the time of the crash, which involved a population of an estimated 25,000 Model S cars. On 8 July 2016, the NHTSA requested Tesla Motors provide the agency detailed information about the design, operation and testing of its Autopilot technology. The agency also requested details of all design changes and updates to Autopilot since its introduction, and Tesla's planned updates schedule for the next four months.

According to Tesla, 'neither autopilot nor the driver noticed the white side of the tractor-trailer against a brightly lit sky, so the brake was not applied.' The car attempted to drive full speed under the trailer, 'with the bottom of the trailer impacting the windshield of the Model S.' Tesla also stated that this was Tesla’s first known autopilot death in over 130 million miles (208 million km) driven by its customers with Autopilot engaged. According to Tesla there is a fatality every 94 million miles (150 million km) among all type of vehicles in the U.S. However, this number also includes fatalities of the crashes, for instance, of motorcycle drivers with pedestrians. In July 2016 the U.S.

(NTSB) opened a formal investigation into the fatal accident while the Autopilot was engaged. The NTSB is an investigative body that only has the power to make policy recommendations. An agency spokesman said 'It's worth taking a look and seeing what we can learn from that event, so that as that automation is more widely introduced we can do it in the safest way possible.' In January 2017, the NTSB released the report that concluded Tesla was not at fault; the investigation revealed that the Tesla car crash rate dropped by 40 percent after Autopilot was installed. According to Tesla, starting 19 October 2016, all Tesla cars are built with hardware to allow full self-driving capability at the highest safety level (). The hardware includes eight surround cameras and twelve ultrasonic sensors, in addition to the forward-facing radar with enhanced processing capabilities.

The system will operate in 'shadow mode' (processing without taking action) and send data back to Tesla to improve its abilities until the software is ready for deployment via over-the-air upgrades. After the required testing, Tesla hopes to enable full self-driving by the end of 2017 under certain conditions.

Google self-driving car [ ]. Google's in-house In August 2012, Alphabet (then Google) announced that their vehicles had completed over 300,000 autonomous-driving miles (500,000 km) accident-free, typically involving about a dozen cars on the road at any given time, and that they were starting to test with single drivers instead of in pairs. In late-May 2014, Alphabet revealed a new prototype that had no steering wheel, gas pedal, or brake pedal, and was fully autonomous. As of March 2016, Alphabet had test-driven their fleet in autonomous mode a total of 1,500,000 mi (2,400,000 km). In December 2016, Alphabet Corporation announced that its technology would be spun-off to a new subsidiary called. Based on Alphabet's accident reports, their test cars have been involved in 14 collisions, of which other drivers were at fault 13 times, although in 2016 the car's software caused a crash. In June 2015, Brin confirmed that 12 vehicles had suffered collisions as of that date.

Eight involved rear-end collisions at a stop sign or traffic light, two in which the vehicle was side-swiped by another driver, one in which another driver rolled through a stop sign, and one where a Google employee was controlling the car manually. In July 2015, three Google employees suffered minor injuries when their vehicle was rear-ended by a car whose driver failed to brake at a traffic light. This was the first time that a collision resulted in injuries.

On 14 February 2016 a Waymo vehicle attempted to avoid sandbags blocking its path. During the maneuver it struck a bus.

Alphabet stated, 'In this case, we clearly bear some responsibility, because if our car hadn’t moved there wouldn’t have been a collision.' Google characterized the crash as a misunderstanding and a learning experience. Uber [ ] In March 2017, an Uber test vehicle was involved in an accident in Arizona when another car failed to yield, flipping the Uber vehicle. Policy implications [ ] If fully autonomous cars become commercially available, they have the potential to be a with major implications for society.

The likelihood of widespread adoption is still unclear, but if they are used on a wide scale, policy makers face a number of unresolved questions about their effects. One fundamental question is about their effect on travel behavior. Some people believe that they will increase car ownership and car use because it will become easier to use them and they will ultimately be more useful. This may in turn encourage and ultimately total private vehicle use. Others argue that it will be easier to share cars and that this will thus discourage outright ownership and decrease total usage, and make cars more efficient forms of transportation in relation to the present situation. Policy-makers will have to take a new look at how infrastructure is to be built and how money will be allotted to build for autonomous vehicles. The need for traffic signals could potentially be reduced with the adoption of.

Due to smart highways and with the assistance of smart technological advances implemented by policy change, the dependence on may be reduced because of less time being spent on the road by individual cars which could have an effect on policy regarding energy. On the other hand, autonomous vehicles could increase the overall number of cars on the road which could lead to a greater dependence on oil imports if smart systems are not enough to curtail the impact of more vehicles.

However, due to the uncertainty of the future of autonomous vehicles, policy makers may want to plan effectively by implementing infrastructure improvements that can be beneficial to both human drivers and autonomous vehicles. Caution needs to be taken in acknowledgment to and that the use may be greatly reduced if autonomous vehicles are catered to through policy reform of infrastructure with this resulting in job loss and increased.

Other disruptive effects will come from the use of autonomous vehicles to carry goods. Self-driving vans have the potential to make home deliveries significantly cheaper, transforming retail commerce and possibly rendering hypermarkets and supermarkets redundant. As of right now the U.S. Government defines automation into six levels, starting at level zero which means the human driver does everything and ending with level five, the automated system performs all the driving tasks. Also under the current law, manufacturers bear all the responsibility to self-certify vehicles for use on public roads. This means that currently as long as the vehicle is compliant within the regulatory framework, there are no specific federal legal barriers to a highly automated vehicle being offered for sale., an associate professor in the MIT Media lab said, 'Most people want to live in a world where cars will minimize casualties, but everyone wants their own car to protect them at all costs.'

Furthermore, industry standards and best practice are still needed in systems before they can be considered reasonably safe under real-world conditions. Legislation [ ] The 1968, subscribed to by over 70 countries worldwide, establishes principles to govern traffic laws. One of the fundamental prinicples of the Convention has been the concept that a is always fully in control and responsible for the behavior of a vehicle in traffic. The progress of technology that assists and takes over the functions of the driver is undermining this principle, implying that much of the groundwork must be rewritten. States that allow driverless cars public road testing as of 9 Jun 2017. In the United States, a non-signatory country to the Vienna Convention, state vehicle codes generally do not envisage — but do not necessarily prohibit — highly automated vehicles. To clarify the legal status of and otherwise regulate such vehicles, several states have enacted or are considering specific laws.

In 2016, 7 states (Nevada, California, Florida, Michigan, Hawaii, Washington, and Tennessee), along with the, have enacted laws for autonomous vehicles. Incidents such as the first fatal accident by Tesla's Autopilot system have led to discussion about revising laws and standards for autonomous cars. In September 2016, the US and released federal standards that describe how automated vehicles should react if their technology fails, how to protect passenger privacy, and how riders should be protected in the event of an accident. The new federal guidelines are meant to avoid a patchwork of state laws, while avoiding being so overbearing as to stifle innovation. In June 2011, the passed a law to authorize the use of autonomous cars.

Nevada thus became the first jurisdiction in the world where autonomous vehicles might be legally operated on public roads. According to the law, the (NDMV) is responsible for setting safety and performance standards and the agency is responsible for designating areas where autonomous cars may be tested. This legislation was supported by in an effort to legally conduct further testing of its.

The Nevada law defines an autonomous vehicle to be 'a motor vehicle that uses, sensors and coordinates to drive itself without the active intervention of a human operator.' The law also acknowledges that the operator will not need to pay attention while the car is operating itself. Google had further lobbied for an exemption from a ban on distracted driving to permit occupants to send while sitting behind the wheel, but this did not become law. Furthermore, Nevada's regulations require a person behind the wheel and one in the passenger’s seat during tests. Main article: Individual vehicles may benefit from information obtained from other vehicles in the vicinity, especially information relating to traffic congestion and safety hazards. Vehicular communication systems use vehicles and roadside units as the communicating in a peer-to-peer network, providing each other with information.

As a cooperative approach, vehicular communication systems can allow all cooperating vehicles to be more effective. According to a 2010 study by the National Highway Traffic Safety Administration, vehicular communication systems could help avoid up to 79 percent of all traffic accidents. In 2012, computer scientists at the University of Texas in Austin began developing smart intersections designed for autonomous cars. The intersections will have no traffic lights and no stop signs, instead using computer programs that will communicate directly with each car on the road.

Among connected cars, an unconnected one is the weakest link and will be increasingly banned from busy high-speed roads, predicted a Helsinki think tank in January 2016. Public opinion surveys [ ]. This article may contain an excessive amount of that may only interest a specific audience.

Please help by or any relevant information, and removing excessive detail that may be against. (August 2016) () In a 2011 online survey of 2,006 US and UK consumers by Accenture, 49% said they would be comfortable using a 'driverless car'. A 2012 survey of 17,400 vehicle owners by J.D. Power and Associates found 37% initially said they would be interested in purchasing a fully autonomous car.

However, that figure dropped to 20% if told the technology would cost $3,000 more. In a 2012 survey of about 1,000 German drivers by automotive researcher Puls, 22% of the respondents had a positive attitude towards these cars, 10% were undecided, 44% were skeptical and 24% were hostile. A 2013 survey of 1,500 consumers across 10 countries by found 57% 'stated they would be likely to ride in a car controlled entirely by technology that does not require a human driver', with Brazil, India and China the most willing to trust autonomous technology. In a 2014 US telephone survey by Insurance.com, over three-quarters of licensed drivers said they would at least consider buying a self-driving car, rising to 86% if car insurance were cheaper. 31.7% said they would not continue to drive once an autonomous car was available instead. In a February 2015 survey of top auto journalists, 46% predict that either Tesla or Daimler will be the first to the market with a fully autonomous vehicle, while (at 38%) Daimler is predicted to be the most functional, safe, and in-demand autonomous vehicle. In 2015 a questionnaire survey by Delft University of Technology explored the opinion of 5,000 people from 109 countries on automated driving.

Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. 22% of the respondents did not want to spend any money for a fully automated driving system. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Finally, respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data.

The survey also gave results on potential consumer opinion on interest of purchasing an automated car, stating that 37% of surveyed current owners were either 'definitely' or 'probably' interested in purchasing an automated car. In 2016, a survey in Germany examined the opinion of 1,603 people, who were representative in terms of age, gender, and education for the German population, towards partially, highly, and fully automated cars. Results showed that men and women differ in their willingness to use them. Men felt less anxiety and more joy towards automated cars, whereas women showed the exact opposite. The gender difference towards anxiety was especially pronounced between young men and women but decreased with participants’ age. In 2016, a survey, in the United States, showing the opinion of 1,584 people, highlights that '66 percent of respondents said they think autonomous cars are probably smarter than the average human driver'.

People are still worried about safety and mostly the fact of having the car hacked. Nevertheless, only 13% of the interviewees see no advantages in this new kind of cars.

Moral issues [ ] With the emergence of autonomous cars, there are various ethical issues arising. While morally, the introduction of autonomous vehicles to the mass market seems inevitable due to a reduction of crashes by up to 90% and their accessibility to disabled, elderly, and young passengers, there still remain some ethical issues that have not yet been fully solved. Those include, but are not limited to: the moral, financial, and criminal responsibility for crashes, the decisions a car is to make right before a (fatal) crash, privacy issues, and potential job loss.

There are different opinions on who should be held liable in case of a crash, in particular with people being hurt. Many experts see the car manufacturers themselves responsible for those crashes that occur due to a technical malfunction or misconstruction. Besides the fact that the car manufacturer would be the source of the problem in a situation where a car crashes due to a technical issue, there is another important reason why car manufacturers could be held responsible: it would encourage them to innovate and heavily invest into fixing those issues, not only due to protection of the brand image, but also due to financial and criminal consequences. However, there are also voices that argue those using or owning the vehicle should be held responsible since they know the risks involved in using such a vehicle. Experts suggest introducing a tax or insurances that would protect owners and users of autonomous vehicles of claims made by victims of an accident. Other possible parties that can be held responsible in case of a technical failure include software engineers that programmed the code for the autonomous operation of the vehicles, and suppliers of components of the AV. Taking aside the question of legal liability and moral responsibility, the question arises how autonomous vehicles should be programmed to behave in an emergency situation where either passengers or other traffic participants are endangered.

A very visual example of the moral dilemma that a software engineer or car manufacturer might face in programming the operating software is described in an ethical thought experiment, the: a conductor of a trolley has the choice of staying on the planned track and running over 5 people, or turn the trolley onto a track where it would only kill one person, assuming there is no traffic on it. There are two main considerations that need to be addressed. First, what moral basis would be used by an autonomous vehicle to make decisions? Second, how could those be translated into software code? Researchers have suggested, in particular, two ethical theories to be applicable to the behavior of autonomous vehicles in cases of emergency: and.

Asimov’s are a typical example of. The theory suggests that an autonomous car needs to follow strict written-out rules that it needs to follow in any situation. Utilitarianism suggests the idea that any decision must be made based on the goal to maximize utility. This needs a definition of utility which could be maximizing the number of people surviving in a crash. Critics suggest that autonomous vehicles should adapt a mix of multiple theories to be able to respond morally right in the instance of a crash. Privacy-related issues arise mainly from the interconnectivity of autonomous cars, making it just another mobile device that can gather any information about an individual.

This information gathering ranges from tracking of the routes taken, voice recording, video recording, preferences in media that is consumed in the car, behavioral patterns, to many more streams of information. The implementation of autonomous vehicles to the mass market might cost up to 5 million jobs in the US alone, making up almost 3% of the workforce. Those jobs include drivers of taxis, buses, vans, trucks, and e-hailing vehicles. Many industries, such as the auto insurance industry are indirectly affected.

This industry alone generates an annual revenue of about $220 billions, supporting 277,000 jobs. To put this into perspective – this is about the number of mechanical engineering jobs.

The potential loss of a majority of those jobs due to an estimated decline of accidents by up to 90% will have a tremendous impact on those individuals involved. Both India and China have placed bans on automated cars with the former citing protection of jobs. In fiction [ ].

On display in Paris, France in October 2002. In film [ ] • A named features in the 1971 to 1978 German of movies similar to 's, but with an electronic brain. (Herbie, also a Beetle, was depicted as an car with its own spirit.) • In the film (1989), starring, the is shown to be able to drive to 's current location with some navigation commands from Batman and possibly some autonomy. • The film (1990), starring, features called Johnny Cabs controlled by artificial intelligence in the car or the occupants.

• The film (1993), starring and set in 2032, features vehicles that can be self-driven or commanded to 'Auto Mode' where a voice-controlled computer operates the vehicle. • The film (1994), starring, set in 2004 and 1994, has autonomous cars. • Another movie, (2000), features an autonomous car commanded. • The film (2002), set in in 2054, features an extended chase sequence involving autonomous cars. The vehicle of protagonist John Anderton is transporting him when its systems are overridden by police in an attempt to bring him into. • The film (2003), during an automobile chase scene; emergency vehicles are taken control by the Terminator in an attempt to kill and Kate Brewster who is played.

• The film, The Incredibles (2004), makes his car autonomous for him while it changes him into his supersuit when driving to save a cat from a tree. At in March 2005. • The film Eagle Eye ( 2008 ) Shia LaBeouf and Michelle Monaghan are driven around in a Porsche Cayenne that is controlled by ARIIA ( a giant supercomputer ). • The film (2004), set in in 2035, features autonomous vehicles driving on highways, allowing the car to travel safer at higher speeds than if manually controlled. The option to manually operate the vehicles is available. • (2017) set in 2029, features. • (2017) opens with cop K waking up in his 3-wheeled autonomous flying vehicle (featuring a separable surveillance roof drone) on approach to a protein farm in northern California.

In literature [ ] Intelligent or self-driving cars are a common theme in literature. Examples include: • In 's science-fiction short story, ' (first published May–June 1953), autonomous cars have ' and communicate via honking horns and slamming doors, and save their human caretaker.

• 's series features intelligent or self-driving vehicles. • In 's novel, (1980), Zeb Carter's driving and flying car 'Gay Deceiver' is at first semi-autonomous and later, after modifications by Zeb's wife Deety, becomes sentient and capable of fully autonomous operation.

• In 's series, a robotic vehicle called 'Solar' is in the 54th book. • ' series,, features intelligent or self-driving vehicles. • In ' novels (2006) and (2010) driverless cars and motorcycles are used for attacks in a software-based.

The vehicles are modified for this using and and are also able to operate as. In television [ ] • ' Season 2, episode 6, Gone in 60 Seconds, features three seemingly normal customized vehicles, a 2009 Roadster, a E90 and a, and one stock luxury, being remote-controlled by a computer hacker. • ', season 18, episode 4 of 2014 TV series features a Japanese autonomous car that takes part in the -style car race. • and, the in the 1982 TV series, were sentient and autonomous. • 'Driven', series 4 episode 11 of the 2003 TV series features a robotic vehicle named 'Otto,' part of a high-level project of the Department of Defense, which causes the death of a Navy Lieutenant, and then later almost kills Abby. • The TV series ' features a silver/grey armored assault vehicle, called The Defender, which masquerades as a flame-red 1992 RT/10 and later as a 1998 cobalt blue.

The vehicle's sophisticated computer systems allow it to be controlled via remote on some occasions. • ' episode ' briefly features a self-driving SUV with a touchscreen interface on the inside. • Bull has a show discussing the effectiveness and safety of self-driving cars in an episode call E.J. See also [ ].

I began work on my first robot about two years ago. For no particular reason, I decided to begin with a line following robot and truth be told, my first attempt at building it was a complete failure. Looking back at my efforts, I believe, for a beginner, I was rather too ambitious. The circuit that I had designed had a bunch of unnecessary stuff which I then believed would give my robot an edge over the others. But it never worked and I had to start all over from scratch. In my second attempt, I managed to get the robot on track. As delighted as I was with my first robot, it was nowhere near where I wanted it to be.

I took it to a couple of competitions and not much to my surprise, it failed in both of them. I knew it was time to make some major changes in the design.

In my third attempt, (actually, it wasn’t the third, it was a revision of my second attempt) I updated the firmware and came up with a much more stable and accurate version. It performed well when tested and much to my delight, it finished first in two competitions and second in another. Though I was proud with what I had achieved, I felt that the robot was visually rather unappealing. And you’ll come to know why from its picture given above. It had wires running all over it and I had no other option but to rebuild it.

A month or so later, I had the robot all ready and that is the current version of my first robot. In this instructable I will guide you through the steps that I've followed in building the current version of my robot. It’s one of those robots which belong to the “scratch-built” category.

You might find it difficult to find all the parts that I have used. So I insist you to read through the steps that I have followed, and then implement it in your own way with the parts that you've found. This instructable requires that you are familiar with the following: • Soldering and related equipments • Hand tools like screwdrivers, wire cutters and strippers • Reading schematics and connection diagrams • C/C++ programming for AVR microcontrollers (optional) Step 1: Gather the Parts. Given below is a list of components that I have used in building this robot. The number of sensors to be used actually depends on the complexity of the course which the robot is supposed to cover.

If the course contains sharp 90 degree turns, intersections and acute angle turns, you will have to go with at least 4 sensors. The robot presented here has a total of nine sensors - eight of them arranged in a line and the other in front of the line at the center.

Choosing the number 8 has the advantage that you can represent the status of all eight sensors using a single byte with each bit representing the state of one sensor. This will be discussed in detail in later steps. Step 3: Batteries.Which One to Choose? Batteries come in different varieties and choosing the right one for your application can be a daunting task. Autonomous mobile robots require batteries that last long and are light in weight. Lithium-ion batteries are a good choice. But they are quite costly and require a dedicated charger.

The one shown in the picture is what I have used. It is a Lithium-ion battery pack rated at 11.1V 3000mAh 2C. It has separate cables for charging and discharging.

I suggest you read this excellent article on batteries written by ladyada. You can find it. Step 4: The Voltage Regulator. The robot need a constant 5V supply for the microcontroller, the sensor array and the LCD. In the first version of my robot, the 7805 linear regulator handled this job.

After a couple of trial runs, I noticed that the regulator was getting heated up even with a heatsink attached to it. So in the later version, I decided to use a switching regulator and now it works fine. SELECTING THE COMPONENTS From the datasheet of LM2576 switching regulator (Page 12, Figure 4), you will find the value of the inductor that is to be used. It depends on the maximum input voltage and maximum output current. In my instance, the maximum input voltage is about 12V and maximum output current does not exceed 200mA. So I must have chosen a 680uH inductor. But unfortunately I couldn’t find one.

All I got was a 390uH and it worked just fine. The value of the input and output capacitors aren’t that critical. Any value above 100uF, 25V should work. The diode should be a fast recovery diode – preferably a Schottky. The one that I have used is 1N5822. The heatsink is not necessary, however I have added one as a measure of safety. Given below are the schematic and board layout files of the voltage regulator designed in Cadsoft Eagle.

You can also download the pdf files from below. You will have to get it ready on PCB or you can make it on a perfboard. The sensor array has a total of nine IR emitter-detector pairs. Eight of these are positioned in a line with a spacing of 18mm in between. The spacing is so chosen that the robot can cover tracks of width between 20mm and 40mm without having the need to change the program. The spacing between an emitter and the corresponding receiver is 10mm.

The sensor array circuit can be considered as two parts-one for the emitter array and the other for the detectors. Let's see each of these in detail. DESIGNING THE IR EMITTER ARRAY When you open the schematic file, you will notice that the IR emitters are connected in a series-parallel form. I obtained this design from.

All I had to do was enter the specs of the LED and the array (given below) and the wizard showed me the arrangement that consumes the least amount of power. Source voltage = 5V Diode forward voltage = 1.5V Diode forward current = 5mA No. Of LEDs in your array = 9 The transistor shown in the picture is used to turn the IR emitters ON and OFF. I have used a 2N2222 but you can use any other n-p-n transistor. The emitters are turned ON only when a reading is to be taken. This method reduces the total power consumed by the sensor array and most importantly, the effect of ambient light on sensor values is also reduced.

This is accomplished by taking two readings - one with the emitters ON and the other with the emitters OFF. Subtracting the sensor values obtained in two cases, we will obtain a value that is independent of ambient light. DESIGNING THE IR DETECTOR ARRAY The IR detector circuit is essentially a potential divider with the IR detector connected in series with a resistor. The eight sensor array is read using the ADC and the ninth sensor is read using Pin 29 (PC7) of Atmega32. For the eight detectors, I have used a 10K resistor array and for the ninth detector I have used a separate 10K resistor. The motor driver is a small circuitry that controls the power supplied to the motor based on the input from a microcontroller. The module that I have used is based on the L298 motor driver IC.

It can drive two motors independently. The operating voltage is between 8V and 48V and the module can handle a maximum current of 2A per motor. PIN FUNCTIONALITY - I1 and I2 are logic input pins corresponding to output pins OP1 and OP2. These output pins are connected to the left motor. - I3 and I4 are logic input pins corresponding to output pins OP3 and OP4. These output pins are connected to the right motor. - EA is the enable input for OP1 and OP2.

- EB is the enable input for OP3 and OP4. - VCC and GND are the supply and ground terminals for driving the motor. - 5V and GN are the supply input to the logic pins. CONNECTIONS - EA, EB and 5V pins are connected to 5V supply. - VCC is connected to battery output.

- I1 is connected to Pin 19 (PWM pin) - I2 is connected to Pin 20 - I3 is connected to Pin 18 (PWM pin) - I4 is connected to Pin 21 SPEED AND DIRECTION CONTROL The microcontroller is programmed to generate a square wave of 500Hz frequency on pins 18 and 19. The dutycycle of these signals can be controlled independently. When a logic 0 is written to Pin 20 and if dutycycle on Pin 19 is 100%, the left motor rotates in forward direction at full speed. When a logic 1 is written to Pin 21 and if dutycycle on Pin 18 is 0%, the left motor rotates in the opposite direction at full speed. So the logic level at pins 20 and 21 determines the direction of rotation and dutycycle on Pins 18 and 19 determines the speed of rotation. Step 7: Wiring the 'Brain'.

Now for those who have had little experience with perfboards, this might seem as a difficult task but believe me it is a lot simpler. All you have to do is wire the standalone circuit (shown in the picture) and then add some berg connectors. If you haven't used a perfboard before, I would suggest you watch this excellent video -. Referring to the pin diagram of Atmega32, the connections to be made are given below: Pins 33 to 40 - sensor array (analog pins) Pins 31 to 28 - sensor array (gnd, vcc, ninth sensor and emitter ON/OFF pins) Pin 25 - Top Push Button Pin Pin 24 - Right Push Button Pin Pin 23 - Middle Push Button Pin Pin 22 - Left Push Button Pin Pins 18 to 21 - motor control pins Pins 1 to 3 - LCD Control Pins (RS, R/W and Enable) Pins 14 to 17 - LCD Data pins (DB4 to DB7) Pins 6 to 11 - ISP header Step 8: Charging and Programming Headers.

I first attached the motor mount and the wheel to both the motors. The shaft of the motor is 6mm in diameter and has an M3 thread hole for attaching the wheels. The motors are then attached to either end of a pipe extender so that the axis of the motor shafts are perfectly aligned.

The whole system is attached to the bottom of the enclosure. Metal studs of 8mm length provide the necessary spacing between the wheel and the enclosure. The wires from the motors are taken out through the pipe extender and are given to the enclosure.

Step 12: Fixing the Caster Wheel. Now that the hardware is all ready, let's move on to the programming part. But before starting out, you have to be ready with the following stuff. • AVR programmer - the one that I have used is.

If you have already got another programmer for AVR you can use that one too. • USB cable - you will need it to connect the programmer with your PC or laptop. • - this is a software bundle that comes with a compiler, a programmer and a debugger for the AVR series of microcontrollers. You will have to download and install it. • The winAVR comes with an editor - the Programmer's Notepad. But the one that I have used is Notepad++ Portable with winAVR plug-in installed in it.

You can download it from below. Now the programmer is connected as shown in the picture. Before uploading the program, we will have to configure the AVR. This is done by writing a pair of bytes called fusebits. This is discussed in detail in the next step.

You can download all the libraries and the program from below. Fusebits are a pair of bytes that configures several parameters of an AVR microcontroller. It decides the frequency at which the AVR operates, the start-up delay required, and many more. I have calculated the required fuses using the Engbedded AVR Fuse Calculator. I have attached an MS-DOS Batch file which you can download. To write the fusebits, connect the programmer to the AVR and run this batch file.

You should be able to see the DOS screen shown above. In the next few steps, we will see some of the important commands used in the program. There are a total of four libraries, one each for the LCD, the sensor, the motors and the push buttons. The three functions used in the library are: init_pushbutton() - activates internal pull-up on push button pins. Call this function once before using any other function of this library. Get_single_button_press() - checks whether any button is pressed.

If it detects any keypress, it waits for that particular key to be released and then returns the corresponding keycode. Wait_for_button_press() - waits until a key is pressed. Upon detecting a keypress, it waits for that particular key to be released. Then it returns the corresponding keycode. Step 20: Motor Library Commands. Two functions are available under the header file init_motors() - this function sets the motor driver control pins to output mode.

Speed control is achieved by Pulse Width Modulation (PWM) technique. This function should be called once before using the function given below. Set_motors() - sets the speed and direction of the two motors. Speed is any value between +250 and -250. A negative value indicates that the direction of rotation is backwards. Set_motors(250, 250) – move forward at full speed set_motors(0, 0) – stop the motors set_motors(-125, -125) – move backward at half the full speed Step 21: LCD Library Commands. The library has got a lot of functions but only a few are required for displaying data.

They are given below. Init_lcd() - sets lcd data and control pins to outpout mode and initializes the LCD. This function should be called once prior to using any other function in this library. Print_string() - prints a text at the specified location. Print_string(2, 2, 'Hello World') displays 'Hello World' (without the double quotes) starting from the 2nd position of the 2nd row of the LCD. Print_integer() - prints an integer of specified number of digits at the specified location. Print_integer(2, 1, number, 3) displays the value present in 'number' starting from the 1st position of 2nd row.

The value can have a maximum of 3 digits. Clear_screen() - clears the display. Step 22: Sensor Library Commands. The functions used in the library are, init_sensors() - initializes 10-bit ADC of Atmega32 and also sets the IR emitter control pin in output mode. This function should be called before using any other function in this library.

Emitters_on() - turns the IR emitters ON. Emitters_off() - turns the IR emitters OFF.

Get_sensors_binary() - reads the state of all 8 sensors and return the value as a byte. Count_binary() - takes a byte as argument and returns the number of bits that are set to 1.

This is used along with get_sensors_binary() to find out the number of sensors that are over the line. Get_front_sensor() - reads the status of the 9th sensor. This function returns a '1' if the sensor is over the line, otherwise it returns a '0'. The function calibrate_sensors() will be discussed separately. The remaining functions are not used in the main program. Krypton Egg Dos Download. I will leave it to you to find out what their purpose is. Step 23: Algorithm.that's What Matters!

The algorithm is developed keeping in mind the following rules: • at any intersection, if there exists a forward path, then the robot should take that path and if it doesn't exist, then the robot is free to choose any path. • if there is any discontinuity in the line, then the robot has to move forward until it detects the line. • there will not be any dead ends in the track.

Given below are three steps which is repeatedly executed when the robot moves over the line. Step1 The sensor readings are taken and stored in a byte with each bit representing the status of one particular sensor. If a bit is 1, then the corresponding sensor is over the line and if the bit is 0, then it is not on the line. The following examples will make it clear.

If the value of the byte is 00011000, only the two sensors in the middle are over the line and if the value of the byte is 10000000, only the left most sensor is over the line. Step2 A count of the number of sensors that are over the line is taken. The count may be any value between 0 and 8. Step3 For the count obtained, the sensor reading is compared with a set of values. If a compare match is found, then the speed and direction of both motors are adjusted accordingly. Given below are some of the values with which the sensor reading is compared.

Given next to each value is the action that is to be taken. 0000000 - no line detected. Continue previous action. 10000000 - turn sharply to the left. 00000001 - turn sharply to the right. Picture 1 shows the two most common sections which the robot will encounter.

When the robot is over a straight line, both the motors are driven at full speed. But when the robot detects a turn, say to the right as shown in the picture, the right motor is slowed down so that the robot turns right. The amount by which the motor is slowed down depends on the degree of the turn. Picture 2 shows six different sections that are tough to handle. The next six steps perform a detailed analysis on how the robot detects each of these six sections and how it reacts to it.

Picture 3 is a flowchart that depicts the series of events that takes place when a turn is detected. Step 25: Sharp 90 Degree Turn. To upload the program, first we need to set up the hardware. For this, connect the programmer to the robot and the USB cable from the programmer to your PC or laptop (as shown in Step 17). The program has to be compiled before it can be uploaded.

To compile the code, open the main.c file in Notepad++ Portable. Select Macro and click on winAVR Compile. Alternately, you can type Ctrl+R to compile.

After compiling the code, the console window will be as shown above. Now to upload the code, go to Macro and select winAVR Program (or type Ctrl+U). Observe the console window. Now that the program is uploaded, the next step is to calibrate the line sensors. Step 32: Line Sensor Calibration. For the robot to detect the line accurately, its sensors should be calibrated. The method that I have used is the median-filter technique.

It is explained in detail below. The calibration code can be obtained from the calibrate_sensors() function under the header file. The array of eight sensors is placed over the bright surface (here it’s the white line). When the calibration command is received, a set of 100 readings is taken.

For each sensor, the median of their 100 readings represents its 'max value'. Next, the sensor array is placed over the dark surface (here it’s the black background) and the same procedure is repeated.

This time, the median represents the 'min value'. The threshold value of each sensor is determined from its ‘max value’ and ‘min value’ using the formula given below. Threshold = min value + (max value - min value) /2 If the value read by a sensor is greater than its threshold value, then that sensor is over the bright surface, otherwise it is over the dark surface. The minimum, maximum and threshold values of each sensor are then written to the EEPROM memory of Atmega32 so that calibration values can be retrieved every time when the robot is turned on.

Note: The ninth sensor cannot be calibrated by software as it is connected to an input pin of Atmega32. The output from the ninth sensor is either a logic 1 or a logic 0 and it depends on the value of the resistor used along with its detector.

I have picked a 10K resistor for this. But I recommend that you use a potentiometer instead. Step 33: Testing the Line Sensors. There are some parameters that you can change depending upon the nature of the track where you test your robot.

They are given below. LINE COLOR If the color of the line is dark (usually black) when compared to that of the background (usually white), you will have to edit the library as shown in the picture above. MOTOR SPEED There are two parameters that define the speed at which the robot moves. MAXSPEED determines the maximum speed of the robot on a scale from 0 to 250. You can change this value to set the desired speed of the robot. ROTATE_SPEED determines the speed with which the robot takes a turn. These two parameters can be adjusted in the main.c file.

After 35 steps, this instructable is complete. Now all you got to do is build one on your own and explore the tracks.