Autonomous car

For the wider application of artificial intelligence to automobiles, see Vehicle Automation.

An autonomous car, also known as robotic or informally as driverless, is an autonomous vehicle capable of fulfilling the human transportation capabilities of a traditional car. As an autonomous vehicle, it is capable of sensing its environment and navigating on its own. A human may choose a destination, but is not required to perform any mechanical operation of the vehicle.

Autonomous cars are not in widespread use, but their introduction could produce several direct advantages:

Indirect advantages are anticipated as well. Adoption of robotic cars could reduce the number of vehicles worldwide,[4][5] reduce the amount of space required for vehicle parking,[6] and reduce the need for traffic police and vehicle insurance.

Autonomous vehicles sense the world with such techniques as laser, radar, lidar, GPS and computer vision. Advanced control systems interpret the information to identify appropriate navigation paths, as well as obstacles and relevant signage. Autonomous vehicles typically update their maps based on sensory input, such that they can navigate through uncharted environments.

Autonomous vehicles are only legal to operate on public roads in one jurisdiction in the world: the U.S. state of Nevada. The law to authorize the use of autonomous vehicles was passed in June 2011, with the support of Google.[7][8][9] The Google driverless car is one of the leading projects in this field, alongside programs such as the 2getthere passenger vehicles from the Netherlands and the entrants of the DARPA Grand Challenge in the USA.

Contents

History

An early representation of the autonomous car was Norman Bel Geddes's Futurama exhibit sponsored by General Motors at the 1939 World's Fair, which depicted electric cars powered by circuits embedded in the roadway and controlled by radio.[10]

In the 1980s a vision-guided Mercedes-Benz robotic van, designed by Ernst Dickmanns and his team at the Bundeswehr University Munich in Munich, Germany, achieved 100 km/h on streets without traffic. Subsequently, the European Commission began funding the €800 million EC EUREKA Prometheus Project on autonomous vehicles (1987–1995).

Also in the 1980s the DARPA-funded Autonomous Land Vehicle (ALV) in the United States achieved the first road-following demonstration that used laser radar (Environmental Research Institute of Michigan), computer vision (Carnegie Mellon University and SRI), and autonomous robotic control (Carnegie Mellon and Martin Marietta) to control a robotic vehicle up to 30 km/h. In 1987, HRL Laboratories (formerly Hughes Research Labs) demonstrated the first off-road map and sensor-based autonomous navigation on the ALV. The vehicle travelled over 600m at 3 km/h on complex terrain with steep slopes, ravines, large rocks, and vegetation.

In 1994, the twin robot vehicles VaMP and Vita-2 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, albeit semi-autonomously with human interventions. They demonstrated autonomous driving in free lanes, convoy driving, and lane changes left and right with autonomous passing of other cars.

In 1995, Dickmanns´ re-engineered autonomous S-Class Mercedes-Benz took a 1600 km 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 175 km/h on the German Autobahn, with a mean time between human interventions of 9 km, 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.

In 1995, the Carnegie Mellon University Navlab project achieved 98.2% autonomous driving on a 5,000 km (3,100 mi) "No hands across America" trip. This car, however, was semi-autonomous by nature: it used neural networks to control the steering wheel, but throttle and brakes were human-controlled.

In 1996, Alberto Broggi of the University of Parma launched the ARGO Project, which worked on enabling a modified Lancia Thema to follow the normal (painted) lane marks in an unmodified highway.[11] 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 vehicle had only two black-and-white low-cost video cameras on board, and used stereoscopic vision algorithms to understand its environment, as opposed to the "laser, radar - whatever you need" approach taken by other efforts in the field.

Three US Government funded military efforts known as Demo I (US Army), Demo II (DARPA), and Demo III (US Army). Demo III (2001)[12] demonstrated the ability of unmanned ground vehicles to navigate miles of difficult off-road terrain, avoiding obstacles such as rocks and trees. James Albus at NIST provided the Real-Time Control System which is a hierarchical control system. Not only were individual vehicles controlled (e.g. throttle, steering, and brake), but groups of vehicles had their movements automatically coordinated in response to high level goals.

In 2010 VisLab ran VIAC, the VisLab Intercontinental Autonomous Challenge, a 13,000 km test run of autonomous vehicles. Four driverless electric vans successfully ended the drive from Italy to China, arriving at the Shanghai Expo on 28 October, 2010. It was the first intercontinental trip ever with autonomous vehicles.[13]

The ParkShuttle in Rotterdam is an example of a driverless car/minibus.[14]

Many companies such as General Motors, Volkswagen, Audi, BMW, Volvo, and Google have begun testing driverless car systems. General Motors has stated that they will begin testing driverless cars by 2015, and they could be on the road by 2018.[15] Volvo has begun to develop an almost-autonomous 'road train' system for highways which could be integrated in cars by 2020.[16]

In 2006 the United Kingdom government's 'Foresight' think-tank revealed a report which predicts a RFID-tagged driverless cars on UK's roads by 2056,[17] and the Royal Academy of Engineering claims that driverless trucks could be on Britain's motorways by 2019.[18]

Autonomous vehicles have also been used in mining: Since December 2008, Rio Tinto has been testing the Komatsu Autonomous Haulage System - the worlds's first commercial autonomous mining haulage system - in the Pilbara iron ores mine, Western Australia. Rio Tinto has reported benefits in health, safety and productivity. In November 2011, Rio Tinto signed a deal to greatly expand its fleet of driverless trucks.[19] Additional mining systems include Sandvik Automine (for underground loaders) and autonomous hauling from Caterpillar Inc.

Legislation

One of the most significant obstacles to the proliferation of driverless cars is the fact that they are illegal in every US state except one.[20]

The Nevada Legislature passed a law in June 2011 to authorize the use of autonomous vehicles. Nevada became the first jurisdiction in the world where driverless vehicles can be legally operated on public roads. The bill was signed into law by Nevada's Governor on June 16, 2011. According to the law, the Nevada Department of Transportation (NDOT) is now responsible for setting safety and performance standards and the agency is responsible for designating areas where driverless cars may be tested.[7][8][21] This legislation was supported by Google in an effort to legally conduct further testing of its Google driverless car.[9]

The law, introduced in March 2011 as bill AB511, defines an autonomous vehicle "to mean a motor vehicle that uses artificial intelligence, sensors and global positioning system 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 text messages while sitting behind the wheel, but this did not become law.[22][23][9]

Vehicular communication systems

See also: Vehicular communication systems

Vehicular Communication Systems are an emerging type of networks in which vehicles and roadside units are the communicating nodes; providing each other with information, such as safety warnings and traffic information. As a cooperative approach, vehicular communication systems can be more effective in avoiding crashes and traffic congestions than if each vehicle tries to solve these problems individually.

Generally vehicular networks are considered to contain two types of nodes; vehicles and roadside stations. Both are Dedicated Short Range Communications (DSRC) devices. DSRC works in 5.9 GHz band with bandwidth of 75 MHz and approximate range of 1000m.[24] The network should support both private data communications and public (mainly safety) communications but higher priority is given to public communications. Vehicular communications is usually developed as a part of Intelligent Transport Systems (ITS). ITS seeks to achieve safety and productivity through intelligent transportation which integrates communication between mobile and fixed nodes. To this end ITS heavily relies on wired and wireless communications.

Fully autonomous system

Fully autonomous driving requires a car to drive itself to a pre-set target using unmodified infrastructure.

Vehicles for roads

Off road vehicles

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

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 could 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 the second competition held in October 2005 five different teams completed the 135-mile (217 km) course, and the Stanford University team won the $2 million prize.
November 3rd, 2007, the third competition was held and $3.5 million dollar in cash prizes, trophies and medals were awarded. Six driverless vehicles were able to complete the 55 miles (89 km) of urban traffic in the 2007 DARPA Urban Challenge rally style race. 1st Place - Tartan Racing, Pittsburgh, PA; 2nd Place - Stanford Racing Team, Stanford, CA; 3rd Place - Victor Tango, Blacksburg, VA.
The German Department of Defense held an exhibition trade show (ELROB) for demonstrating automated vehicles in May 2006. The event included various military automated and remotely operated robots, for various military uses. Some of the systems on display could be ordered and implemented immediately. In August 2007 a civilian version of the event was held in Switzerland.
The Smart team from Switzerland presented "a Vehicle for Autonomous Navigation and Mapping in Outdoor Environments".
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.
In November 2010, the first competition was held, for a winning prize-money of $100 thousand, and the Hanyang University A1 team won the $100 thousand prize.

Systems relying on 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. Such systems already function in many airports, on railroads, and in some European towns.

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 concept along those lines, for purely public transport.)

Groups working on this concept are:

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.

One example that uses this implementation 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.[36][37][38] The test site is a 12-kilometer, high-occupancy-vehicle (HOV) segment of Interstate 15, 16 kilometers north of downtown San Diego.[39]

This effort by the US government seems to have been abandoned because of political and feasibility concerns.[40][41]

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.[42]

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 has been operated since 1999[43] as a public transport system in the city of Capelle aan den IJssel to connect the Rivium business park with the neighboring city of Rotterdam (where the route terminates at the Kralingse Zoom metro station). The system experienced a crash in 2005[44] that proved to be caused by a human error.[45]

Driver-assistance

Some 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. This approach is slowly trickling into standard cars (e.g. improvements to cruise control).

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

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.

Systemic

See also Safety Features.

In film

See also

Notes

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  2. ^ O'Toole (2009) p. 192
  3. ^ a b "Future Car Focus: Robot Cars". http://editorial.autos.msn.com/article.aspx?cp-documentid=1179150. 
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  5. ^ Arth, Michael E. (2010). Democracy and the Common Wealth: Breaking the Stranglehold of the Special Interests.. Golden Apples Media. pp. 363–368. ISBN 978-0-912467-12-2..  Arth claims that this would be possible if almost all private cars requiring drivers, which are not in use and parked 90% of the time, would be traded for public self-driving taxis that would be in near constant use.
  6. ^ "BMW Remote Controlled Parking". http://www.bmwblog.com/2010/10/10/bmw-remote-controlled-parking/.  Parking spaces could be smaller due to a smaller safety margin and no need to open the passenger doors if the passengers exit at their destination.
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  10. ^ O'Toole (2009) pp. 189-192
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  29. ^ Stanford University Junior car
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References

O'Toole, Randal (2009). Gridlock: why we're stuck in traffic and what to do about it. Cato Institute. ISBN 9781935308232. http://books.google.com/books?id=1I8Wuv7P13AC.