Information about Driverless Car

The driverless car concept embraces an emerging family of highly automated cognitive and control technologies, ultimately aimed at a full "taxi-like" experience for car users, but without a human driver. Together with alternative propulsion, it is seen by some as the main technological advance in car technology by 2020.

Driverless passenger programs include the 800 million ECU EUREKA Prometheus Project on autonomous vehicles (1987-1995), the 2getthere passenger vehicles (using the FROG-navigation technology) from the Netherlands, the ARGO research project from Italy, and the DARPA Grand Challenge from the USA. For the wider application of artificial intelligence to automobiles see smart cars.

History

The history of autonomous vehicles started in 1977 with the Tsukuba Mechanical Engineering Lab in Japan. On a dedicated, clearly marked course it achieved speeds of up to 20 miles per hour, by tracking white street markers. Special hardware was necessary, since commercial computers were much slower than they are today.

The breakthrough in autonomous driving came in the 1980s through the work of Ernst Dickmanns and his team at Bundeswehr Universität München. Their vision-guided Mercedes-Benz robot van achieved 60 miles per hour on streets without traffic. The subsequent 800 million Euro EUREKA Prometheus Project on autonomous vehicles (1987-1995) brought further progress. A first culmination point was achieved in 1994, when the twin robot vehicles VITA-2 and VaMP of Daimler-Benz and Ernst Dickmanns of UniBwM drove more than one thousand kilometers on a Paris three-lane highway in standard heavy traffic at speeds up to 130 km/h. They demonstrated autonomous driving in free lanes, convoy driving, and lane changes left and right with autonomous passing of other cars.

The next culmination point was achieved in 1995, when Dickmanns´ re-engineered autonomous S-Class Mercedes-Benz took a 1000 mile trip from Munich in Bavaria to Copenhagen in Denmark and back, using saccadic computer vision and transputers to react in real time. The robot achieved speeds exceeding 110 miles per hour on the German Autobahn, with a mean time between human interventions of 9km, or 95% autonomous driving. Again it drove in traffic, executing manoeuvres to pass other cars. Despite being a research system without emphasis on long distance reliability, it drove up to 158 km without human intervention. (Also in 1995, a semi-autonomous vehicle with human-controlled throttle and brakes (CMU Navlab project) achieved 98.2% autonomous driving on a 3000-mile "No hands across America" trip.)

The abilities of these early vehicles heavily influenced research world-wide, including three DARPA efforts known as Demo I, Demo II, Demo III. Demo III (2001) demonstrated the ability of unmanned ground vehicles to navigate miles of difficult off-road terrain, avoiding obstacles such as rocks and trees.

The challenge

Though the vision of a fully autonomous vehicle is clear, it would be such an upheaval in technology and lifestyle that few dare contemplate a new technology that would simply do it.

Some have argued that the problem is AI-complete -- that a safe and reliable driverless car would need to use all the skills of an ordinary human being, including commonsense reasoning and affective computing. The primary concern is that driverless cars will perform worse than human beings in unexpected circumstances like the following:

• A mattress tied to the top of a truck on the freeway is flapping in the wind. The car should change lanes so that if it comes loose it will not be involved in the ensuing pile up.
• A ball has just bounced across a suburban street. The car should break in case the ball is being chased by a child.
• A jaywalker is standing next to the lane of slow moving downtown traffic. The car should judge whether he is waiting for the car to pass or is just about to step in front of it.
• A medium sized human is standing in an intersection aggressively swinging his arm left to right with his index finger extended. The car should turn right if he is a police officer and should ignore him if not.

However, some are attempting to solve bits and pieces of the problem — either for the benefit of the limited invention created, or explicitly as stepping stones towards a fully driverless car. Though most of the projects are government-sponsored, there is already a significant involvement from the private sector.

The challenges involved in realizing this vision can broadly be divided into the technical and the social. The technical problems are broadly in the design of the sensors and control system required to make such a car work. The social challenge is in getting people to trust the car, getting legislators to permit the car onto the public roads, and untangling the legal issues of liability for any mishaps with no person in charge.

The elements of any solution

The dream of a driverless car seems fantastic, and therefore remote. However, any solution can be broken down to four sub-systems:

• sensors: the car knows where an obstacle is and what is around it;
• navigation: how to get to the target location from the present location;
• motion planning: getting through the next few meters, steering, and avoiding obstacles while also abiding by rules of the road and avoiding harm to the vehicle and others;
• control of the vehicle itself: actuating the system's decisions.

In examining every proposed solution, one should look at the following questions:

• Is this truly a complete system? Does it drive itself door-to-door?
• To what degree is the proposed solution a step towards the complete vision, or is it just a trick?
• Is the car 'autonomous', or would it need changes to the infrastructure?
• How feasible (technically, economically, and politically) would it be to deploy the entire solution?
• Can the system allow for and include existing vehicles driven by humans, or does it need an open field?
• How would it cope with unexpected circumstances?

Recent projects

The work done so far varies significantly in its ambition and its demands in terms of modification of the infrastructure. Broadly, there are three approaches. The first group to be discussed here is the fully autonomous vehicles (PROMETHEUS, DARPA, ARGO) —which are the most ambitious, but none are deployed. The second approach uses various enhancements to the infrastructure (either an entire area, or specific lanes) to create a self-driving closed system. Such systems already function in many airports, on railroads, and in some European towns. The third approach is to incrementally remove requirements from the human driver, by various "assistance" systems. This approach is slowly trickling into standard cars (e.g. improvements to cruise control).

An important concept that cuts across several of the efforts is vehicle platoons. In order to better utilize road-space, vehicles are assembled into ad-hoc train-like "platoons", where the driver (either human or automatic) of the first vehicle makes all decisions for the entire platoon. All other vehicles simply follow the lead of the first vehicle.

Fully autonomous

These technologies are the most ambitious: They allow a car to drive itself following a pre-set target, until it gets there all on its own. The most prominent project in this vein was the 800 million Euro EUREKA Prometheus Project on autonomous vehicles (1987-1995). Among its culmination points were the fast twin robot vehicles VITA-2 and VaMP of Daimler-Benz and Ernst Dickmanns, driving long distances in heavy traffic (see history above). The final goal of safe door-to-door transportation in arbitrary environments is not yet reached though.

Free-ranging military vehicles

There are three clusters of activity relating to free-ranging off-road cars. Some of these projects are military-oriented.

• US military DARPA Grand Challenge

Main article: DARPA Grand Challenge

The US Department of Defense announced on the July 30, 2002 a "Grand Challenge", for US-based teams to produce a vehicle that can autonomously navigate and reach a target in the desert of the south western USA.

In March 2004, the first competition was held, for a prize-money of $1 million. Not one of the 25 entrants completed the course. However, in October 2005 five different teams completed the 135-mile (217 km) course, and the Stanford University team won the $2 million prize.

Following the 2004 failure, in which several cars were distracted by the "race" to the detriment of basic technology that would allow for actual completion, the 2005 teams were focused on the challenge at hand, and did not seek to develop generic solutions, or a particularly speedy car.

The sensors were based on visual, Radar, and laser technologies. The navigational course was pre-programmed, and the motion planning and obstacle avoidance were handled by on-board computers - many of the entrants used eight or more computers to manage the car. Though the vehicles were equipped to avoid collision, they did not have any notion of rules-of-the road - but simply regarded each other as static obstacles.

Five cars finished the course of the 2005 DARPA Grand Challenge. It is interesting to compare them to the earlier VaMP robot car of Mercedes-Benz and Ernst Dickmanns. In 2005, the DARPA cars drove 212 km without human intervention. In 1995, the VaMP drove up to 158 km without human intervention. The DARPA cars drove on a dirt road flattened by a steamroller. The VaMP drove on the Autobahn. In both cases the road boundaries were easily identifiable by computer vision. Like many commercial cars, the DARPA cars used GPS navigation, essentially driving from one waypoint to the next (almost 3000 waypoints for the entire course, several waypoints per curve). Like humans, the VaMP drove by vision only. The DARPA cars reached speeds up to 40 km/h. The VaMP reached speeds up to 180 km/h. So the VaMP was more than four times faster although its computer processors apparently were 1000 times slower. The DARPA cars did not encounter any traffic but a few stationary obstacles. The VaMP drove in traffic around moving obstacles, passing other cars.

In 2007, the Darpa Grand Urban Challenge will take place on November 3, 2007 at the site of the now-closed George Air Force Base (currently used as Southern California Logistics Airport), in Victorville, California.^[1] The course will involve a 60-mile (96 km) urban area course, to be completed in less than 6 hours. Rules will include obeying all traffic regulations while negotiating with other traffic and obstacles and merging into traffic. The 2007 Urban Grand Challenge is trying to repeat some of the feats of the VaMP and its twin vehicle VITA-2.

For a more complete description the DARPA Grand Challenge see the official web site and the press coverage.[1][2][3] The US military has several projects applying autonomous vehicle technologies for military purposes.[4][5]

• European Land-Robot Trial (ELROB)

Not to be outdone by the USA, the German Department of Defense announced an event similar to the DARPA Grand Challenge, held in May 2006. Unlike the DARPA event, it is not in the spirit of a challenge, but of a demonstration or trade show. The event included various military autonomous and remotely-operated robots, for various military uses. ELROB is less cutting-edge than the Grand Challenge, but more practical, in that some of the systems on display could be ordered and implemented immediately.

In August 2007 a civilian version of the event will be held in Switzerland.

The most impressive effort in the fully driverless category was The Smart team from Switzerland, presenting "a Vehicle for Autonomous Navigation and Mapping in Outdoor Environments". For pictures of their ELROB demo, see this.

• The Israeli Military-Industrial Complex

As a followup from its success with Unmanned Combat Air Vehicles, and following the construction of the Israeli West Bank barrier there has been significant interest in developing a fully automated border-patrol vehicle. Two projects, by Elbit Systems and Israel Aircraft Industries are both based on the locally-produced Armored "Tomcar" and have the specific purpose of patrolling barrier fences against intrusions.

The "SciAutonics II" team in the 2004 DARPA Challenge used Elbit's version of the Tomcar.

ARGO

ARGO is an Italian project (1996-2001) to allow a car to follow the normal (painted) lane marks in an unmodified highway. The culmination of the project was a journey of 2,000 km over six days on the motorways of northern Italy dubbed MilleMiglia in Automatico, with an average speed of 90 km/h. 94% of the time the car was in fully automatic mode, with the longest automatic stretch being 54 km.

The ARGO vehicle, a modified Lancia Thema, had only two black-and-white low-cost video cameras on board, and used stereoscopic vision algorithms to understand its environment. This is in stark contrast to the "laser, radar - whatever you need" approach taken by other efforts in the field.

The project was run by the universities of Parma and Pavia, coordinated by Alberto Broggi, and financed by the Italian government.

Pre-built infrastructure

The following projects were conceived as practical attempts to use available technology in an incremental manner to solve specific problems, like transport within a defined campus area, or driving along a stretch of motorway. The technologies are proven, and the main barrier to widespread implementation is the cost of deploying the infrastructure.

Dual mode transit - monorail

There is a family of projects, all currently still at the experimental stage, that would combine the flexibility of a private automobile with the benefits of a monorail system. The idea is that privately-owned cars would be built with the ability to dock themselves onto a public monorail system, where they become part of a centrally managed, fully computerized transport system—more akin to a driverless train system (as already found in airports) than to a driverless car. This idea is also known as Dual mode transit. (See also Personal rapid transit for another interesting concept along those lines, for purely public transport.)

Groups working on this concept are:

• RUF (Denmark)
• BiWay (UK)
• ATN (New Zealand)
• TriTrack (Texas, United States)

Automated highway systems

Automated highway systems (AHS) are an effort to construct special lanes on existing highways that would be equipped with magnets or other infrastructure to allow vehicles to stay in the center of the lane, while communicating with other vehicles (and with a central system) to avoid collision and manage traffic. Like the dual-mode monorail, the idea is that cars remain private and independent, and just use the AHS system as a quick way to move along designated routes. AHS allows specially equipped cars to join the system using special 'acceleration lanes' and to leave through 'deceleration lanes'. When leaving the system each car verifies that its driver is ready to take control of the vehicle, and if that is not the case, the system parks the car safely in a predesignated area.

Some implementations use radar to avoid collisions and coordinate speed.

The most impressive system of this type built so far is the AHS demo of 1997 near San Diego, sponsored by the US government, in coordination with the State of California and Carnegie Mellon University. The test site is a 12-kilometer, high-occupancy-vehicle (HOV) segment of Interstate 15, 16 kilometers north of downtown San Diego. The event generated much press coverage. The technology is the subject of a book.

This concerted effort by the US government seems to have been pretty much abandoned because of social and political forces, above all else the desire to create a less futuristic and more marketable solution.

As of 2007, a three-year project is underway to allow robot controlled vehicles, including buses and trucks, to use a special lane along 20 Interstate 805. The intention is to allow the vehicles to travel at shorter following distances and thereby allow more vehicles to use the lanes. The vehicles will still have drivers since they need to enter and exit the special lanes. The system is being designed by Swoop Technology, based in San Diego county.^[2]

Free-ranging on grid

Frog Navigation Systems(the Netherlands) applies the FROG (free-ranging on grid) technology. The technology consists of a combination of autonomous vehicles and a supervisory central system. The company's purpose-built electric vehicles locate themselves using odometry readings, recalibrating themselves occasionally using a "maze" of magnets embedded in the environment, and GPS. The cars avoid collisions with obstacles located in the environment using laser (long range) and ultra-sonic (short-range) sensors.

The vehicles are completely autonomous and plan their own routes from A to B. The supervisory system merely administers the operations and directs traffic where required. The system has been applied both indoors and outdoors, and in environments where 100+ automated vehicles are operational (container port). At this time the system is not suited yet for running the sheer number of vehicles encountered in urban settings. The company also has no intention of developing such technology at this time.

The FROG system is deployed for industrial purposes in factory sites, and is marketed as a pilot public transport system in the city of Capelle aan den IJssel by its subsidiary 2getthere. This system experienced an accident that proved to be caused by a Human error.

Frog Navigation Systems is one of few fully commercial companies in this field.

Driver-assistance

Though these products and projects do not aim explicitly to create a fully autonomous car, they are seen as incremental stepping-stones in that direction. Many of the technologies detailed below will probably serve as components of any future driverless car — meanwhile they are being marketed as gadgets that assist human drivers in one way or another.

Driver-assistance mechanisms are of several distinct types, sensorial-informative, actuation-corrective, and systemic.

Sensorial-informative

These systems warn or inform the driver about events that may have passed unnoticed, such as

• Lane Departure Warning System (LDWS), for example from Iteris, see also this overview.
• Rear-view alarm, to detect obstacles behind. This system gets activated when the reverse gear is engaged.
• Visibility aids for the driver, to cover blind spots and enhanced vision systems such as radar, wireless vehicle safety communications and night vision.
• Infrastructure-based, driver warning/information-giving systems, such as those developed by the Japanese government

Actuation-corrective

These systems modify the driver's instructions so as to execute them in a more effective way, for example the most widely deployed system of this type is ABS; conversely power steering is not a control mechanism, but just a convenience - it is not involved in decision making.

• Anti-lock braking system (ABS) (also Emergency Braking Assistance (EBA), often coupled with Electronic brake force distribution (EBD), which prevents the brakes from locking and losing traction while braking. This shortens stopping distances in most cases and, more important, it allows the driver to steer the vehicle while braking.
• Traction control (TCS) actuates brakes or reduces throttle to restore traction if driven wheels begin to spin.
• Four wheel drive (AWD) with a centre differential. Distributing power to all four wheels lessens the chances of wheel spin. It also suffers less from oversteer and understeer.
• Electronic Stability Control (ESC)(also known for Mercedes-Benz proprietary Electronic Stability Program (ESP), Acceleration Slip Regulation (ASR) and Electronic differential lock (EDL)). Uses various sensors to intervene when the car senses a possible loss of control. The car's control unit can reduce power from the engine and even apply the brakes on individual wheels to prevent the car from understeering or oversteering.
• Dynamic steering response (DSR) corrects the rate of power steering system to adapt it to vehicle's speed and road conditions.

A review of the overall "feel" to actuation-correction in a Jaguar XK convertible.

Driver-assistance preview from Popular Science.

Note: The electronic differential lock (EDL) employed by Volkswagen is not - as the name suggests - a differential lock at all. Sensors monitor wheel speeds, and if one is rotating substantially faster than the other (i.e. slipping) the EDL system momentarily brakes it. This effectively transfers all the power to the other wheel^[3].

Systemic

• Automatic parking: e.g. technology from Toyota selling for $700, with a 70% take-up rate. The Lexus LS can park itself (parallel/reverse) via the 'Advanced Parking Guidance System' – though only controlling the steering.
• Follow another car on a motorway ("Enhanced" or "adaptive" cruise control), like The Ford, Honda or Vauxhall(GM).
• Nissan's "Distance Control assist"
• Death Brake; there is a move to introduce deadman's braking into automotive application, primarily heavy vehicles, and there may also be a need to add penalty switches to cruise controls.

A good collection of these technologies is available at Automotive component manufacturers' sites, such as Siemens VDO Automotive or [6] Delphi (Ford)].

Interesting stuff from GM-Opel.

A good summary of how far things have progressed without any true automated driving is provided by The Economist

See also Safety Features.

Existing and missing technologies

In order to drive a car, a system would need to:

1. Understand its immediate environment (Sensors)
2. Know where it is and where it wants to go (Navigation)
3. Find its way in the traffic (Motion planning)
4. Operate the mechanics of the vehicle (Actuation)

Arguably, 2 1/2 of these problems are already solved: Navigation and Actuation completely, and Sensors partially, but improving fast. The main unsolved part is the motion planning.

Sensors

Sensors employed in driverless cars vary from the minimalist ARGO project's monochrome stereoscopy to mobileye's inter-modal (video, infra-red, laser, radar) approach. The minimalist approach imitates the human situation most closely, while the multi-modal approach is "greedy" in the sense that it seeks to obtain as much information as is possible by current technology, even at the occasional cost of one car's detection system interfering with another's.

Mobileye is a well respected company who makes detection systems for cars, which are currently only used for driver assistance, but are eminently suitable for a full-fledged driverless car. This video demonstrates the capabilities of the system: all pedestrians, cars, motorbikes etc. are clearly displayed in video, with a frame around them and the distance between "our" car and the object observed. The system also detects the objects' motion (direction and speed) and can so calculate relative speeds, and predict collisions.

• Japanese infra-red article
• some things from the DARPA challenge....
• Road-sign recognition

Navigation

The ability to plot a route from where the vehicle is to where the user wants to be has been available for several years. These systems, based on the US military's Global Positioning System are now available as standard car fittings, and use satellite transmissions to ascertain the current location, and an on-board street database to derive a route to the target. The more sophisticated systems also receive radio updates on road blockages, and adapt accordingly.

See the main article on Automotive navigation systems.

Motion Planning

[7]
[8] (video on autonomous navigation)
This is current research problem. See the main article on the subject Motion planning.

Control of vehicle

As automotive technology matures, more and more functions of the underlying engine, gearbox etc. are no longer directly controlled by the driver by mechanical means, but rather via a computer, which receives instructions from the driver as inputs and delivers the desired effect by means of electronic throttle control, and other drive-by-wire elements. Therefore, the technology for a computer to control all aspects of a vehicle is well understood.

Work done in simulation

While developing control systems for real cars is very costly in terms of both time and money, much work can be done in simulations of various complexity. Systems developed using simpler simulators can gradually be transferred to more complex simulators, and in the end to real vehicles. Some approaches that rely on learning requires starting in a simulation to be viable at all, for example evolutionary robotics approaches - see this example.

Social issues

• Getting people to trust the car
• Getting legislators to permit the car onto the public roads
• Untangling the legal issues of liability for any mishaps with no person in charge.
• Despair of progress in the foreseeable future: The UK government seems to see little progress until 2056. See Silicon Networks article and CNET.co.uk News.

Motivations

As nearly all car crashes (particularly fatal ones) are caused by human driver error, driverless cars would effectively eliminate nearly all hazards associated with driving as well as driver fatalities and injuries (traveling by car is currently one of the most deadly forms of transportation, with over a million deaths annually worldwide). This would be especially helpful to people that drive to bars and inebriate themselves; the ability for a car to shuttle them home would practically eliminate drunk driving crashes.

Having the equivalent of a personal chauffeur would be a great convenience:

• Time spent commuting could be used for work, leisure, or rest.
• Parking in difficult areas becomes less of a concern as the car can park itself away from a busy airport, for example, and come back when called on a cell-phone.
• Taxiing children to school, activities and friends would become solely a matter of granting permission for the car to handle the child's request.
• Allow the visually (and otherwise) impaired to travel independently.
• One could sleep overnight during long road trips.

A driverless car would also be a boon to economic efficiency, as cars can be made lighter and more space efficient with the absence of safety technologies rendered redundant with computerized driving. Also the technology would make transportation more efficient and reliable: there may be autonomous or remote-controlled delivery trucks dispatched around the clock to pick up and deliver goods. Moreover, driverless cars would reduce traffic congestion by allowing cars to travel faster and closer together.

Social Costs

The social costs of this innovation are similar to those of other past technologies: Unemployment, expense and the elimination of the "old way of doing things". See also Luddites.

As with any new labor-saving technology, this would lead to mass layoffs in the driving, cargo, and distribution industries. Taxis would also be automated, effectively eliminating a source of income for the less skilled. A similar if smaller impact is expected in the roadside-catering and other ancillary businesses. However, history shows that any such economic impact on jobs leads to economic benefits elsewhere that create employment, though often not for the exact same people displaced by the new technology.

In order to recoup the development costs, and in order to maximise the profit opportunity that any exciting novelty presents, driverless cars will initially be significantly more expensive than manual cars.

Driving as a personal hobby and sport, and indeed the entire car-oriented sub-culture would be effectively eliminated. However, for those willing to pay for the extra feature, there could be an option to switch between manual and automated driving to make up for that.

Discussion & Future

Some systems control everything centrally, and in some the vehicle is truly autonomous in the sense that it "thinks" about its own situation in the first person - such a system can integrate with Humans that think in first person.

Conversely. a system that centrally manages everything, though easier to build from a conceptual and engineering point of view, would face horrendous economic barriers because of the costs of converting an entire city or country to the new system at once. In order to be compatible with humans the "first person" point of view is key. This is for three reasons:

1. a distributed scheme in which each component (car) takes care of itself reduces complexity
2. a system that has the concept of first-person operation can understand what a human driver is up to.
3. for the human driver to understand what the driverless car is doing, it needs to operate and "think" in as similar a way to a human as practical (and safe).

See also Coping, see Heidegger.

Key players

International

The European Union has a multi-billion Euro programme to support Research and Development by ad-hoc consortia from the various member countries, called Framework Programmes for Research and Technological Development. Several of these projects pertain to the subject of driverless cars, e.g.:

• The CyberCars project gathered much useful data about the actual and possible deployments of Driverless Cars for public transport. The main system discussed is based on FROG.

Many of the EU-sponsored projects are coordinated by a group called Ertico.

There are several national associations around the world that are active in research in the field of intelligent transportation systems, a term that seems to encompass anything which applies technology to the improvement of transport. In recent years there has been a trend in this field to move efforts away from the more visionary projects, such as driverless cars, to the more short-term, such as public transport and traffic management. Many of these organizations are government sponsored, and they all cooperate at some level or another. Some of the countries involved are: the USA, Australia, Korea (south), Taiwan, India--(specifically Intelligent vehicles), and Japan, specifically a cruise assist effort (see below). A more complete list of its organizations can be found here.

Governments

• USA:
• Federal Government:
• [9]
• [10]
• [11]
• Specifically, California
• Caltrans PATH
• Japan has an impressive advanced-highway effort, doing "advanced cruise-assist", with an impressive future plan.

Universities and professional bodies

• Berkeley: http://www.path.berkeley.edu/PATH/Publications/Videos/
• VisLab: Artificial Vision and Intelligent Systems Lab at Parma University
• Virginia Tech
• IEEE has a Society (the Intelligent Transportation Systems Society), runs an important scientific Journal, and organizes conferences
• Japanese Automobile Research Institution
• Carnegie Mellon University Navlab
• UC Berkeley - California PATH

Commercial interests

• Daimler-Chrysler
• Daimler-Chrysler
• [12]
• Vauxhall set to launch the world's first driverless car in Europe - November 2005

Voluntary and hobbyist groups

• Autonomous Robots Magazine http://springerlink.metapress.com/
• RCTC / AMERICAN INDUSTRIAL MAGIC entered 3 vehicles in the 2004 DARPA callenge.

In film

• The 1990 film Total Recall, starring Arnold Schwarzenegger, features taxis apparently controlled by artificial intelligence; it is not clear, however, whether these are truly autonomous vehicles or simply conventional vehicles driven by androids.
• Another Arnold Schwarzenegger movie, The 6th Day (2000), features a driverless car in which Michael Rapaport sets the destination and vehicle drives itself while Rapaport and Schwarzenegger converse.
• The 2002 film Minority Report, set in Washington, D.C. in 2054, features an extended chase sequence involving driverless personal cars. The vehicle of protagonist John Anderton is transporting him when its systems are overridden by police in an attempt to bring him into custody; Anderton is unable to control the vehicle, and has to break out of it to evade the authorities.
• The 2004 film I, Robot features vehicles with automated driving on future highways, allowing the car to travel safely at higher speeds than if manually controlled.
• Kitt, the automated TransAm in the TV series Knight Rider could drive by itself upon command

See also

• Unmanned ground vehicle
• Vehicle Infrastructure Integration
• Vehicle
• Robot
• Autonomous robot
• Artificial intelligence
• Rules of the road
• Traffic
• Road
• Road transport

References

External links

• A history of the driverless car.
• An agglomeration of links
• A good overview from the EEtimes.
• http://www.ercim.org/publication/Ercim_News/enw42/fraichard.html
• Slashdot discussions: animal-inspired robotics about The Economist article above.
• engadget discussion about how computer-controlled cars can reduce congestion.
• Driverless cars as assistive technology
• Transportation Communications Newsletter
• Descriptions of EU Research Programs aimed at integrating Automated Transit
• Self-Driving Car at HowStuffWorks.