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Vehicular automation

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The ESA Seeker autonomous rover during tests at Paranal[1]
Automated vehicle system technology hierarchy

Vehicular automation is using technology to assist or replace the operator of a vehicle such as a car, truck, aircraft, rocket, military vehicle, or boat.[2][3][4][5][6] Assisted vehicles are semi-autonomous, whereas vehicles that can travel without a human operator are autonomous.[3] The degree of autonomy may be subject to various constraints such as conditions. Autonomy is enabled by advanced driver-assistance systems (ADAS) of varying capacity.

Related technology includes advanced software, maps, vehicle changes, and outside vehicle support. The benefits of viewing automated driving from a sociotechnical systems perspective has been discussed.[7]

Autonomy presents varying issues for road, air, and marine travel. Roads present the most significant complexity given the unpredictability of the driving environment, including diverse road designs, driving conditions, traffic, obstacles, and geographical/cultural differences.[8]

Autonomy implies that the vehicle is responsible for all perception, monitoring, and control functions.[9]

SAE autonomy levels

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The SAE classification of levels is based on the role of the driver, rather than the vehicle's capabilities, although these are related in the form of a "driving mode" (also known as a "driving scenario"). The mode is determined by both an operational design domain (ODD) and a "dynamic driving requirement". The ODD is the circumstance in which the car is driving, and the driving requirement is what the system must do while remaining safe within the boundaries of that ODD. These two things define the SAE level.[10]

J3016 automation levels[10]
Mode Level Summary Description Responsibility for
Direction & speed Monitoring environment Fallback
N/A 0 No Automation Full-time performance by the driver of all aspects of driving, even when "enhanced by warning or intervention systems" Driver Driver Driver
Some 1 Driver assistance Driving mode-specific control by an ADAS of either steering or speed ADAS uses information about the driving environment; driver is expected to perform all other driving tasks.
System
2 Partial automation Driving mode-specific execution by one or more ADAS for both steering and speed
3 Conditional automation Driving mode-specific control by an ADAS of all aspects of driving Driver must appropriately respond to a request to intervene. System
Many 4 High automation If a driver does not respond appropriately to a request to intervene, the car can stop safely. System
All 5 Full automation System controls the vehicle under all conditions and circumstances.

Technology

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Software

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Autonomous vehicle software generally contains several different modules that work together to enable self-driving capabilities.[11][12][13] The perception module ingests and processes data from various sensors, such cameras, LIDAR, RADAR, and ultrasonic SONAR, to create a comprehensive understanding of the vehicle's surroundings.[14] The localization module uses 3D point cloud data, GPS, IMU, and mapping information to determine the vehicle's precise position, including its orientation, velocity, and angular rate.[12][15] The planning module takes inputs from both perception and localization to compute actions to take, such as velocity and steering angle outputs.[16] These modules are typically supported by machine learning algorithms, particularly deep neural networks,[17] which enable the vehicle to detect objects, interpret traffic patterns,[18] and make real-time decisions.[19] Furthermore, modern autonomous driving systems increasingly employ sensor fusion techniques that combine data from multiple sensors to improve accuracy and reliability in different environmental conditions.[20][21]

Perception

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The perception system is responsible for observing the environment. It must identify everything that could affect the trip, including other vehicles, pedestrians, cyclists, their movements, road conditions, obstacles, and other issues.[22] Various makers use cameras, radar, lidar, sonar, and microphones that can collaboratively minimize errors.[22][23]

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Navigation systems are a necessary element in autonomous vehicles. Global navigation satellite systems (GNSS) are used for navigation by air, water, and land vehicles, particularly for off-road navigation.

For road vehicles, two approaches are prominent. One is to use maps that hold data about lanes and intersections, relying on the vehicle's perception system to fill in the details. The other is to use highly detailed maps that reduce the scope of real-time decision-making but require significant maintenance as the environment evolves.[17] Some systems crowdsource their map updates, using the vehicles themselves to update the map to reflect changes such as construction or traffic used by the entire vehicle fleet.[24]

Another potential source of information is the environment itself. Traffic data may be supplied by roadside monitoring systems and used to route vehicles to best use a limited road system.[25] Additionally, modern GNSS enhancement technologies, such as real-time kinematic (RTK) and precise point positioning (PPP), enhance the accuracy of vehicle positioning to sub-meter level precision, which is crucial for autonomous navigation and decision-making.[26]

History

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Stanford Cart and Hydraulic Arm

The "Stanford Cart", created by Hans Moravec in the late 1970s while he was a graduate student, was the first experimental autonomous vehicle. It was a precursor to both NASA's Moon and Mars Lander projects as it was known at the time that radio signal lag times would have made anything other than autonomous control impractical. The box rested on 4 bicycle wheels, and had a camera, battery and a radio antenna connecting it wirelessly to a remote computer. It could also be steered remotely.[27] Morovic was able to get the Cart to navigate around large obstacles in a 100 foot long room, albeit it would take 5 hours as the cart would frequently stop as the computer processed images which it analyzed and then responded with navigation instructions.

Approximately 20 years later the Robotics Lab at Carnegie Mellon University developed ALVINN (Autonomous Land vehicle in a Neural Network) a vehicle with 3 onboard Sun Microsystems computers that, using a camera and a laser range finder, could slowly drive itself down a road by monitoring the white divider line.[28][29]

Automated vehicles in European Union legislation refer specifically to road vehicles (car, truck, or bus).[30] For those vehicles, a specific difference is legally defined between advanced driver-assistance system and autonomous/automated vehicles, based on liability differences.

AAA Foundation for Traffic Safety tested two automatic emergency braking systems: some designed to prevent crashes and others that aim to make a crash less severe. The test looked at popular models like the 2016 Volvo XC90, Subaru Legacy, Lincoln MKX, Honda Civic, and Volkswagen Passat. Researchers tested how well each system stopped when approaching moving and nonmoving targets. It found that systems capable of preventing crashes reduced vehicle speeds by twice that of the systems designed to mitigate crash severity. When the two test vehicles traveled within 30 mph of each other, even those designed to lessen crash severity avoided crashes 60 percent of the time.[31]

Sartre

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The SAfe Road TRains for the Environment (Sartre) project's goal was to enable platooning, in which a line of cars and trucks (a "train") follow a human-driven vehicle. Trains were predicted to provide comfort and allow the following vehicles to travel safely to a destination. Human drivers encountering a train could join and delegate driving to the human driver.[32]

Tests

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Self-driving Uber vehicles were tested in Pittsburgh, Pennsylvania. The tests were paused after an autonomous car killed a woman in Arizona.[33][34] Automated busses have been tested in California.[35] In San Diego, California, an automated bus test used magnetic markers. The longitudinal control of automated truck platoons used millimeter wave radio and radar. Waymo and Tesla have conducted tests. Tesla FSD allows drivers to enter a destination and let the car take over.

Risks and liabilities

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Ford offers Blue Cruise, technology that allows geofenced cars to drive autonomously.[36]

Drivers are directed to stay attentive, and safety warnings are implemented to alert the driver when corrective action is needed.[37] Tesla, Incorporated has one recorded incident that resulted in a fatality involving the automated driving system in the Tesla Model S.[38] The accident report reveals the accident was a result of the driver being inattentive and the autopilot system not recognizing the obstruction ahead.[38] Tesla has also had multiple instances where the vehicle crashed into a garage door. According to the book "The Driver in the Driverless Car: How Your Technology Choices Create the Future," Tesla automatically performs an update overnight. The morning after the update, the driver used his app to "summon" his car, and it crashed into his garage door.

Another flaw with automated driving systems is that unpredictable events, such as weather or the driving behavior of others, may cause fatal accidents due to sensors that monitor the surroundings of the vehicle not being able to provide corrective action.[37]

To overcome some of the challenges for automated driving systems, novel methodologies based on virtual testing, traffic flow simulation and digital prototypes have been proposed,[39] especially when novel algorithms based on Artificial Intelligence approaches are employed which require extensive training and validation data sets.

Implementing automated driving systems poses the possibility of changing built environments in urban areas, such as expanding the suburban regions due to the increased ease of mobility.[40]

Challenges

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Around 2015, several self-driving car companies including Nissan and Toyota promised self-driving cars by 2020. However, the predictions turned out to be far too optimistic.[41]

There are still many obstacles in developing fully autonomous Level 5 vehicles, which is the ability to operate in any conditions. Currently, companies are focused on Level 4 automation, which is able to operate under certain environmental circumstances.[41]

There is still debate about what an autonomous vehicle should look like. For example, whether to incorporate lidar to autonomous driving systems is still being argued. Some researchers have come up with algorithms using camera-only data that achieve the performance that rival those of lidar. On the other hand, camera-only data sometimes draw inaccurate bounding boxes, and thus lead to poor predictions. This is due to the nature of superficial information that stereo cameras provide, whereas incorporating lidar gives autonomous vehicles precise distance to each point on the vehicle.[41]

Technical challenges

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  • Software Integration: Because of the large number of sensors and safety processes required by autonomous vehicles, software integration remains a challenging task. A robust autonomous vehicle should ensure that the integration of hardware and software can recover from component failures.[42]
  • Prediction and trust among autonomous vehicles: Fully autonomous cars should be able to anticipate the actions of other cars like humans do. Human drivers are great at predicting other drivers' behaviors, even with a small amount of data such as eye contact or hand gestures. In the first place, the cars should agree on traffic rules, whose turn it is to drive in an intersection, and so on. This scales into a larger issue when there exists both human-operated cars and self-driving cars due to more uncertainties. A robust autonomous vehicle is expected to improve on understanding the environment better to address this issue.[42]
  • Scaling up: The coverage of autonomous vehicles testing could not be accurate enough. In cases where heavy traffic and obstruction exist, it requires faster response time or better tracking algorithms from the autonomous vehicles. In cases where unseen objects are encountered, it is important that the algorithms are able to track these objects and avoid collisions.[42]

These features require numerous sensors, many of which rely on micro-electro-mechanical systems (MEMS) to maintain a small size, high efficiency, and low cost. Foremost among MEMS sensors in vehicles are accelerometers and gyroscopes to measure acceleration around multiple orthogonal axes—critical to detecting and controlling the vehicle's motion.

Societal challenges

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One critical step to achieve the implementation of autonomous vehicles is the acceptance by the general public. It provides guidelines for the automobile industry to improve their design and technology. Studies have shown that many people believe that using autonomous vehicles is safer, which underlines the necessity for the automobile companies to assure that autonomous vehicles improve safety benefits. The TAM research model breaks down important factors that affect the consumer's acceptance into: usefulness, ease to use, trust, and social influence.[43]

  • The usefulness factor studies whether or not autonomous vehicles are useful in that they provide benefits that save consumers' time and make their lives simpler. How well the consumers believe autonomous vehicles will be useful compared to other forms of transportation solutions is a determining factor.[43]
  • The ease to use factor studies the user-friendliness of the autonomous vehicles. While the notion that consumers care more about ease to use than safety has been challenged. It still remains an important factor that has indirect effects on the public's intention to use autonomous vehicles.[43]
  • The trust factor studies the safety, data privacy and security protection of autonomous vehicles. A more trusted system has a positive impact on the consumer's decision to use autonomous vehicles.[43]
  • The social influence factor studies whether the influence of others would influence consumer's likelihood of having autonomous vehicles. Studies have shown that the social influence factor is positively related to behavioral intention. This might be due to the fact that cars traditionally serve as a status symbol that represents one's intent to use and his social environment.[43]

Regulatory challenges

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Real-time testing of autonomous vehicles is an inevitable part of the process. At the same time, vehicular automation regulators are faced with challenges to protect public safety and yet allow autonomous vehicle companies to test their products. Groups representing autonomous vehicle companies are resisting most regulations, whereas groups representing vulnerable road users and traffic safety are pushing for regulatory barriers. To improve traffic safety, the regulators are encouraged to find a middle ground that protects the public from immature technology while allowing autonomous vehicle companies to test the implementation of their systems.[44] Regulators face daunting challenges such as jurisdictional ambiguities, the rapid obsolescence of current technologies and the lack of future-looking cost-benefit data to support regulatory development.[45] There have also been proposals to adopt the aviation automation safety regulatory knowledge into the discussions of safe implementation of autonomous vehicles, due to the experience that has been gained over the decades by the aviation sector on safety topics.[46]

Ground vehicles

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In some countries, specific laws and regulations apply to road traffic motor vehicles (such as cars, bus and trucks) while other laws and regulations apply to other ground vehicles such as tram, train or automated guided vehicles making them to operate in different environments and conditions.

Road traffic vehicles

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An automated driving system is defined in a proposed amendment to Article 1 of the Vienna Convention on Road Traffic:

(ab) "Automated driving system" refers to a vehicle system that uses both hardware and software to exercise dynamic control of a vehicle on a sustained basis.

(ac) "Dynamic control" refers to carrying out all the real-time operational and tactical functions required to move the vehicle. This includes controlling the vehicle's lateral and longitudinal motion, monitoring the road environment, responding to events in the road traffic environment, and planning and signalling for manoeuvres.[47]

This amendment will enter into force on 14 July 2022, unless it is rejected before 13 January 2022.[48]

An automated driving feature must be described sufficiently clearly so that it is distinguished from an assisted driving feature.

— SMMT[49]

There are two clear states – a vehicle is either assisted with a driver being supported by technology or automated where the technology is effectively and safely replacing the driver.

— SMMT[49]

Ground vehicles employing automation and teleoperation include shipyard gantries, mining trucks, bomb-disposal robots, robotic insects, and driverless tractors.

There are many autonomous and semi-autonomous ground vehicles being made for the purpose of transporting passengers. One such example is the free-ranging on grid (FROG) technology which consists of autonomous vehicles, a magnetic track and a supervisory system. The FROG system is deployed for industrial purposes in factory sites and has been in use since 1999 on the ParkShuttle, a PRT-style 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[50] that proved to be caused by a human error.[51]

Applications for automation in ground vehicles include the following:

Research is ongoing and prototypes of autonomous ground vehicles exist.

Cars

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Extensive automation for cars focuses on either introducing robotic cars or modifying modern car designs to be semi-autonomous.

Semi-autonomous designs could be implemented sooner as they rely less on technology that is still at the forefront of research. An example is the dual mode monorail. Groups such as RUF (Denmark) and TriTrack (USA) are working on projects consisting of specialized private cars that are driven manually on normal roads but also that dock onto a monorail/guideway along which they are driven autonomously.

As a method of automating cars without extensively modifying the cars as much as a robotic car, Automated highway systems (AHS) aims to construct lanes on highways that would be equipped with, for example, magnets to guide the vehicles. Automation vehicles have auto-brakes named as Auto Vehicles Braking System (AVBS). Highway computers would manage the traffic and direct the cars to avoid crashes.

In 2006, The European Commission has established a smart car development program called the Intelligent Car Flagship Initiative.[52] The goals of that program include:

There are further uses for automation in relation to cars. These include:

Singapore also announced a set of provisional national standards on January 31, 2019, to guide the autonomous vehicle industry. The standards, known as Technical Reference 68 (TR68), will promote the safe deployment of fully driverless vehicles in Singapore, according to a joint press release by Enterprise Singapore (ESG), Land Transport Authority (LTA), Standards Development Organisation and Singapore Standards Council (SSC).[55]

Shuttle

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Parkshuttle
Navya Autonom Shuttle
Easymile EZ10
King Long Apolong

Since 1999, the 12-seat/10-standing ParkShuttle has been operating on an 1.8 kilometres (1.1 mi) exclusive right of way in the city of Capelle aan den IJssel in The Netherlands. The system uses small magnets in the road surface to allow the vehicle to determine its position. The use of shared autonomous vehicles was trialed around 2012 in a hospital car park in Portugal.[56] From 2012 to 2016, the European Union funded CityMobil2 project examined the use of shared autonomous vehicles and passenger experience including short term trials in seven cities. This project led to the development of the EasyMile EZ10.[57]

In the 2010s, self-driving shuttle became able to run in mixed traffic without the need for embedded guidance markers.[58] So far the focus has been on low speed, 20 miles per hour (32 km/h), with short, fixed routes for the "last mile" of journeys. This means issues of collision avoidance and safety are significantly less challenging than those for automated cars, which seek to match the performance of conventional vehicles. Many trials have been undertaken, mainly on quiet roads with little traffic or on public pathways or private roadways and specialised test sites.[citation needed] The capacity of different models varies significantly, between 6-seats and 20-seats. (Above this size there are conventional buses that have driverless technology installed.)

In December 2016, the Jacksonville Transportation Authority has announced its intention to replace the Jacksonville Skyway monorail with driverless vehicles that would run on the existing elevated superstructure as well as continue onto ordinary roads.[59] The project has since been named the "Ultimate Urban Circulator" or "U2C" and testing has been carried out on shuttles from six different manufacturers. The cost of the project is estimated at $379 million.[60]

In January 2017, it was announced the ParkShuttle system in the Netherlands will be renewed and expanded including extending the route network beyond the exclusive right of way so vehicles will run in mixed traffic on ordinary roads.[61] The plans were delayed and the extension into mixed traffic was expected in 2021.[62]

In July 2018, Baidu stated it had built 100 of its 8-seat Apolong model, with plans for commercial sales.[63] As of July 2021, they had not gone into volume production.

In August 2020, it was reported there were 25 autonomous shuttle manufacturers,[64] including the 2GetThere, Local Motors, Navya, Baidu, Easymile, Toyota and Ohmio.

In December 2020, Toyota showcased its 20-passenger "e-Palette" vehicle, which is due to be used at the 2021 Tokyo Olympic Games.[65] Toyota announced it intends to have the vehicle available for commercial applications before 2025.[66]

In January 2021, Navya released an investor report which predicted global autonomous shuttle sales will reach 12,600 units by 2025, with a market value of EUR 1.7 billion.[67]

In June 2021, Chinese maker Yutong claimed to have delivered 100 models of its 10-seat Xiaoyu 2.0 autonomous bus for use in Zhengzhou. Testing has been carried out in a number of cities since 2019 with trials open to the public planned for July 2021.[68]

Self-driving shuttles are already in use on some private roads, such as at the Yutong factory in Zhengzhou where they are used to transport workers between buildings of the world's largest bus factory.[69]

In Hong Kong, the police and other workers use driverless vehicles.[70]

Trials

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A large number of trials have been conducted since 2016, with most involving only one vehicle on a short route for a short period of time and with an onboard conductor. The purpose of the trials has been to both provide technical data and to familiarize the public with the driverless technology. A 2021 survey of over 100 shuttle experiments across Europe concluded that low speed – 15–20 kilometres per hour (9.3–12.4 mph) – was the major barrier to implementation of autonomous shuttle buses. The current cost of the vehicles at €280,000 and the need for onboard attendants were also issues.[71]

Company/LocationDetails
Navya "Arma" in Neuhausen am Rheinfall In October 2016, BestMile started trials in Neuhausen am Rheinfall, claiming to be the world's first solution for managing hybrid fleets with both autonomous and non-autonomous vehicles.[72] The test ended in October 2021.[73]
Local Motors "Olli" At the end of 2016, the Olli was tested in Washington D.C.[74] In 2020, a four-month trial was undertaken at the United Nations ITCILO campus in Turin, Italy to provide transport shuttle to employees and guests within the campus.[75]
Navya "Autonom"Navya claimed in May 2017 to have carried almost 150,000 passengers across Europe[76] with trials in Sion, Cologne, Doha, Bordeaux and the nuclear power plant at Civaux as well as Las Vegas[77] and Perth.[78] Ongoing public trials are underway in Lyon, Val Thorens and Masdar City. Other trials on private sites are underway at University of Michigan since 2016,[79] at Salford University and the Fukushima Daini Nuclear Power Plant since 2018.[80]
Texas A&M In August 2017, a driverless four seat shuttle was trialed at Texas A&M university as part of its "Transportation Technology Initiative" in a project run by academics and students on the campus.[81][82] Another trial, this time using Navya vehicles, was run in 2019 from September to November.[83]
RDM Group "LUTZ Pathfinder"In October 2017, RDM Group began a trial service with two seat vehicles between Trumpington Park and Ride and Cambridge railway station along the guided busway, for possible use as an after hours service once the regular bus service has stopped each day.[84]
EasyMile "EZ10"EasyMile has had longer term trials at Wageningen University and Lausanne as well as short trials in