User:Tiffany Ong/Vehicular automation

Lead Section
Original:

Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent system to assist a vehicle's operator. These features and the vehicles employing them may be labeled as intelligent or smart. A vehicle using automation for difficult tasks, especially navigation, may be referred to as semi-autonomous. A vehicle relying solely on automation is consequently referred to as robotic or autonomous. After the invention of the integrated circuit, the sophistication of automation technology increased. Manufacturers and researchers subsequently added a variety of automated functions to automobiles and other vehicles.

Changed:

Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent system to assist a vehicle's operator. These features and the vehicles employing them may be labeled as intelligent or smart. A vehicle using automation for difficult tasks, especially navigation, may be referred to as semi-autonomous. A vehicle relying solely on automation is consequently referred to as robotic or autonomous. After the invention of the integrated circuit, the sophistication of automation technology increased. Manufacturers and researchers subsequently added a variety of automated functions to automobiles and other vehicles. The technology involved in implementing autonomous vehicles is very expansive, ranging from technological improvements on the vehicle itself to the environment and objects around the vehicle. As the use of automated vehicles increases, they are becoming more influential in human lives. Although automated vehicles bring various benefits, it also comes with various concerns. Also, there are still technology challenges that autonomous vehicles seek to make a breakthrough in order to make it robust and scalable.

Tiffany

 * new section: will be the last section in the article.

Lack of control
Through the autonomy level (link this to wikiarticle), it is shown that the higher the level of autonomy, the fewer control humans have on their vehicles (highest level of autonomy needing zero human interventions). One of the few concerns regarding the development of vehicular automation is related to the end-users’ trust in the technology that controls automated vehicles [1]. According to a nationally conducted survey made by Kelley Blue Book (link this to wikiarticle) (KBB) in 2016, it is shown that the majority of people would still choose to have a certain level of control behind their own vehicle rather than having the vehicle operate in Level 5 autonomy, or in other words, completely autonomous [2]. According to half of the respondents, the idea of safety in an autonomous vehicle diminishes as the level of autonomy increases [2]. This distrust of autonomous driving systems proved to be unchanged throughout the years when a nationwide survey conducted by AAA Foundation for Traffic and Safety (AAAFTS) in 2019 showed the same outcome as the survey KBB did in 2016. AAAFTS survey showed that even though people have a certain level of trust in automated vehicles, most people also have doubts and distrust towards the technology used in autonomous vehicles, with most distrust in Level 5 autonomous vehicles [3]. It is shown by AAAFTS’ survey that people’s trust in autonomous driving systems increased when their level of understanding increased [3].

Malfunctions
The possibility of autonomous vehicle’s technology to experience malfunctions is also one of the causes of user’s distrust in autonomous driving systems [1]. In fact, it is the concern that most respondents voted for in the AAAFTS survey [3]. Even though autonomous vehicles are made to improve traffic safety by minimizing crashes and their severity [3], they still caused fatalities. At least 113 autonomous vehicle related accidents have occurred until 2018 [4]. In 2015, Google declared that their automated vehicles experienced at least 272 failures, and drivers had to intervene around 13 times to prevent fatalities [5]. Furthermore, other automated vehicles’ manufacturers also reported automated vehicles’ failures, including the Uber car incident [5]. The self-driving Uber car accident that happened in 2018 is one of the examples of autonomous vehicle accidents that are also listed in "List of self-driving car fatalities" (link wikiarticle). One of the reports made by the National Transportation Safety Board (NTSB) showed that the self-driving Uber car was unable to identify the victim in a sufficient amount of time for the vehicle to slow down and avoid crashing into the victim [6].

A prototype of an autonomous Uber car being tested in San Francisco.

(Image: "File:Uber autonomous vehicle prototype testing in San Francisco.jpg" by Dllu is licensed under CC BY-SA 4.0)

Ethical
Another concern related to vehicle automation is its ethical issues. In reality, autonomous vehicles can encounter inevitable traffic accidents. In situations like that, many risks and calculations need to be made in order to minimize the amount of damage the accident could cause [7]. When a human driver encounters an inevitable accident, the driver will take a spontaneous action based on ethical and moral logic. However, when a driver has no control over the vehicle (Level 5 autonomy), the system of an autonomous vehicle is the one who needs to make that instant decision [7]. Unlike humans, autonomous vehicles don’t have reflexes and it can only make decisions based on what it is programmed to do [7]. However, the situation and circumstances of accidents differ from one another, and one decision might not be the best decision for certain accidents. Based on two research made in 2019 [8][9], studies indicate that the implementation of fully automated vehicles in traffic where semi-automated and not automated vehicles are still present might lead to many complications [8]. Some flaws that still need consideration include the structure of liability, distribution of responsibilities [9], efficiency in decision making, and the performance of autonomous vehicles with its diverse surroundings [8].

Daniel
Topic/Section: Spot by Boston Dynamics

Drones Section
Replace:


 * Also see Delivery Drone.
 * A few experiments have been undergone to develop delivery drones for various industries, including packages and food. Innovation is driving this very young market and sees traditional transportation companies compete with start-ups, governments and technological companies like Amazon, for whom delivery is central to its growth strategy.
 * Various industries such as packages and food experimented with delivery drones. Traditional and new transportation companies are competing to dominate the market. For example, UPS Flight Forward (wikilink), Alphabet Wing, and Amazon Prime Air are all programs that have gotten ahead in drone delivery development.
 * https://www.fool.com/investing/2019/12/23/3-companies-looking-to-dominate-drone-delivery.aspx
 * From original “On the other side, other countries have decided to ban, either directly (outright ban) or indirectly (effective ban), the use of commercial drones. The RAND Corporation thus makes the difference between countries forbidding drones and those that have a formal process for commercial drone licensing, but requirements are either impossible to meet or licenses do not appear to have been approved.”
 * Addition:
 * In the US, UPS is the only one with the Part 135 Standard certification that is required to use drones to deliver to real customers.
 * https://www.fool.com/investing/2019/12/23/3-companies-looking-to-dominate-drone-delivery.aspx

Motorcycles

 * Honda motorcycle
 * Inspired by the Uni-cub, Honda implemented their self-balancing technology into their motorcycles. Due to the weight of motorcycles, it is often a challenge for motorcycle owners to keep balance of their vehicles at low speeds or at a stop. Honda’s motorcycle concept has a self-balancing feature that will keep the vehicle upright. It automatically lowers the center of balance by extending the wheelbase. It then takes control of the steering to keep the vehicle balanced. This allows users to navigate the vehicle more easily when walking or driving in stop and go traffic. However, this system is not for high speed driving.
 * https://www.wired.com/2017/01/hondas-self-balancing-motorcycle-perfect-noobs/
 * https://jalopnik.com/harley-davidson-wants-to-make-self-balancing-motorcycle-1843958686
 * BMWs Motorrad Vision concept motorcycle
 * BMW Motorrad developed the ConnectRide self driving motorcycle in order to push the boundaries of motorcycle safety. The autonomous features of the motorcycle include emergency braking, negotiating intersections, assisting during tight turns, and front impact avoidance. These are features similar to current technologies that are being developed and implemented in autonomous cars. This motorcycle can also fully drive on its own at normal driving speed, making turns and returning to a designated location. It lacks the self standing feature that Honda has implemented.
 * https://www.roadandtrack.com/new-cars/car-technology/a23083999/bmw-motorrad-self-driving-motorcycle/
 * Yamaha’s riderless motorcycle
 * “Motoroid” can hold its balance, autonomous driving, recognizing riders and going to a designated location with a hand gesture. Yamaha utilized the “Human beings react a hell of a lot quicker” research philosophy into the motoroid. The idea is that the autonomous vehicle is not attempting to replace human beings, but to augment the abilities of the human with advanced technology. They have tactile feedback such as a gentle squeeze to a rider’s lower back as a reassuring caress at dangerous speeds, as if the vehicle was responding and communicating with the rider. Their goal is to “meld” the machine and human together to form one experience.
 * https://www.iol.co.za/motoring/bikes/self-balancing-yamaha-motorcycle-comes-on-command-12697833
 * Harly-Davidson
 * While their motorcycles are popular, one of the largest problems of owning a Harly-Davidson is the reliability of the vehicle. It is difficult to manage the weight of the vehicle at low speeds and picking it up from the ground can be a difficult process even with correct techniques. In order to attract more customers, they filed a patent for having a gyroscope at the back of the vehicle that will keep the balance of the motorcycle for the rider at low speeds. After 3 miles per hour, the system disengages. However anything below that, the gyroscope can handle the balance of the vehicle which means it can balance even at a stop. This system can be removed if the rider feels ready without it meaning it is modular.
 * https://jalopnik.com/harley-davidson-wants-to-make-self-balancing-motorcycle-1843958686

Watercraft
Sea Machines


 * Sea Machines offers an autonomous system to ships. While it does require a human operator to oversee its actions, the system takes care of a lot of active domain perception and navigation duties that normally a few members of the crew would have to do. They use AI to have situational awareness for different ships within the route. They utilize camera, lidar, and proprietary software to inform the operator of its status.


 * https://sea-machines.com/products
 * https://www.vice.com/en_us/article/ne95qm/autonomous-boats-will-be-here-before-self-driving-cars

Buffautomation


 * Buffautomation, a team formed from the University of Buffalo, creates technology for semi-autonomous features for boats. They started out creating navigation assist technologies for freighters, which is like having another very experienced “first mate” that will look out for the ship. The system helps make twists and turns of difficult waterways.


 * https://www.vice.com/en_us/article/ne95qm/autonomous-boats-will-be-here-before-self-driving-cars
 * https://www.buffalorising.com/2020/05/self-driving-water-taxis-buffalo-automation-speaks-to-our-inventive-past/

Autonomous Marine Systems


 * This Massachusetts based company has led the forefront of unmanned sailing drones. The Datamarans are out autonomously sailing around to collect ocean data. They are created to enable large payload packages. Due to the automated system and their solar panels, they are able to navigate for longer periods of time. More than anything they boast their technologies on advanced metocean surveys which collect “wind velocity profiles with altitude, water current, conductivity, temperature profiles with depth, hi-resolution bathymetry, sub-bottom profiling, magnetometer measurements”
 * https://www.automarinesys.com/mark8
 * https://www.vice.com/en_us/article/ne95qm/autonomous-boats-will-be-here-before-self-driving-cars

Trains
Add Link https://en.wikipedia.org/wiki/List_of_automated_train_systems

Assistance robots (create section)
Spot, from Boston Dynamics


 * This robot is a four-legged nimble robot that was created to be able to navigate through many different terrain outdoors and indoors. It can walk on its own without colliding into anything. It utilizes many different sensors, including 360 vision cameras and gyroscopes. It is able to keep its balance even when pushed over. This vehicle, while it is not intended to be ridden, can carry heavy loads for construction workers or military personnel through rough terrain.
 * https://www.bostondynamics.com

Charles
Challenges of automated driving systems

Section: Still consider making it a separate section or incorporating it to “risks and liabilities” section

Note: please refer to “Charles’s references” for the index of the bibliography

Challenges:
Overview:

Around 2015, several self-driving car companies including Nissan and Toyota promised self-driving cars in 2020. However, the predictions turned out to be far too optimistic.[1]

There are still many obstacles in developing fully autonomous Level 5 vehicles, which is able to operate in any conditions. Currently, companies are focused on Level 4 automation, which is able to operate under certain environmental circumstances.[1]

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 utilizing 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. [1]

Technical Challenges:

 * 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.[2]
 * 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.[2]
 * Scaling up: The coverage of autonomous vehicles testing could never 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's important that the algorithms are able to track these objects and avoid collisions. [2]

Societal Challenges:
One critical step to achieve the implementation of autonomous vehicles is the acceptance by the general public. It is an important ongoing research because 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.[3]

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.[3]

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.[3]

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.[3]

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. [3]

Regulatory Challenges:
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. [4]

Steve
The Technology of Automated Driving


 * New section after Autonomy levels

Technology used in Vehicular Automation

The primary means of implementing autonomous vehicles is through the use of Artificial Intelligence (link). In order for full autonomous vehicles to be implemented, the lower levels of automation must be thoroughly tested and implemented before moving onto the next level[1]. Through implementing autonomous systems, such as navigation, collision avoidance and steering, autonomous vehicle manufacturers work towards the highest level of autonomy by designing and implementing different systems of the car[1]. These autonomous systems along with the use of artificial intelligence methods, autonomous vehicles can use the machine learning aspect of the AI in order for the vehicle to control each of the other autonomous systems and processes. Thus, autonomous vehicle manufacturers are researching and developing appropriate artificial intelligence for the purpose of being implemented in autonomous vehicles[2]. While many of these companies are continuously developing technologies to be implemented into their autonomous vehicles, the general consensus is that technology is still in need of further development before we are anywhere close to implementing fully autonomous vehicles[3].

Arguably one of the most important systems of any autonomous vehicle, the perception system must be fully developed and well-tested in order for autonomy to advance[3]. With the development and implementation of the perception system on autonomous vehicles, much of the safety standards of autonomous vehicles are being addressed by this system, which places an unequivocal emphasis on it to be flawless, as human lives would be subject to harm if a faulty system were to be developed[3]. The main purpose for the perception system is to constantly scan the surrounding environment and determine which objects in the environment pose a threat to vehicles[3]. In a sense, the perception system’s main goal is to act like the human perception, allowing the driver to sense hazards and for them the driver to prepare or correct for these hazards[3]. In terms of the detection part of the perception system, many solutions are being tested for accuracy and compatibility, such as radar, lidar, sonar and actual photography[3].

With the development of these autonomous subsystems of the car, autonomous vehicle manufacturers have already developed systems which act as assistance features on a vehicle. These systems are known as advanced driver-assistance systems (link), and contain systems to do such actions as parallel parking and emergency braking[2]. Along these systems, autonomous navigation systems play a role in the development of autonomous vehicles. In implementing the navigation system, there are two ways in which navigation can be implemented: sensing from one vehicle to another or sensing from infrastructures[2]. These navigation systems would work in tandem with the navigation systems we already have, such as GPS, and be able to process route information, detecting such things as traffic jams, tolls and or road construction. From this information, the vehicle can then take the appropriate action to either avoid the area or plan accordingly[3]. However, there may be problems from using this method, such as outdated information, in which case vehicle to infrastructure communication can play a huge role in constantly having up to date information[3]. An instance of this is having street signs and other regulatory markers display information to the vehicle, which allows the vehicle to make decisions based on the current information[3].

Along with the development of autonomous vehicles, many of these vehicles are expected to be primarily electric, meaning that the main power source of the vehicle will be electric-based rather than fossil fuel-based[1]. Along with that, there comes the extra demand on autonomous vehicle manufacturers to produce higher quality electric cars in order to implement all the autonomous systems associated with the vehicle[4]. However, much of modern-day vehicle components can still be used in autonomous vehicles, such as the use of the automatic transmissions and operator protection equipment like airbags[4].

In consideration of the development of autonomous vehicles, companies also are considering operator preferences and needs in terms of the development. These instances include allowing the user to minimize time, follow a precise route and accommodate any possible disabilities that the operator may have[5]. Along with accommodating the driver, autonomous vehicles also impose a technological factor onto the environment around it, generally needing a higher sense of connectivity in the vehicle’s environment. With this new factor to consider, many urban governments are considering becoming a smart city (link) in order to provide a sufficient foundation for autonomous vehicles[5]. Along these same lines of the vehicle’s environment accommodating the vehicle, the user of these vehicles may also have to be technologically connected in order to operate these autonomous vehicles. With the advent of smartphones, it is predicted that autonomous vehicles will be able to have this connection with the user’s smartphone or other technological device such as a smartphone[5].

Tiffany’s references
[1] T. Liljamo, H. Leiimatainen, and M. Pöllänen, “Transportation Research Part F: Traffic Psychology and Behaviour,” Elsevier, 04-Sep-2018. [Online]. Available: https://dl.uswr.ac.ir/bitstream/Hannan/94143/1/2018%20TransportationResearch%20Volume%2059%20PartA%20November%20%2834%29.pdf. [Accessed: 29-Jul-2020].

[2] "Despite Autonomous Vehicle Intrigue, Americans Still Crave Control Behind The Wheel, According To New Kelley Blue Book Study: Two-Thirds of Consumers Believe Roadways Would Be Safer with Self-Driving Cars," PR Newswire, 2016. Available: http://libproxy.usc.edu/login?url=https://search-proquest-com.libproxy2.usc.edu/docview/1825236192?accountid=14749.

[3] Kim, W., Kelley-Baker, T., Sener, I. N., Zmud, J., Graham, M. & Kolek, S. “Users’ Understanding of Automated Vehicles and Perception to Improve Traffic Safety — Results from a National Survey,” AAA Foundation for Traffic Safety. [Online]. Available: https://aaafoundation.org/wp-content/uploads/2019/12/19-0493_AAAFTS_Emerging-Technology-Report_FINAL-1203.pdf.

[4] S. Wang and Z. Li, “Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches,” PloS one, 28-Mar-2019. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6438496/. [Accessed: 26-Jul-2020].

[5] J. Ainsalu, V. Arffman, M. Bellone, M. Ellner, T. Haapamäki, N. Haavisto, E. Josefson, A. Ismailogullari, B. Lee, O. Madland, R. Madžulis, J. Müür, S. Mäkinen, V. Nousiainen, E. Pilli-Sihvola, E. Rutanen, S. Sahala, B. Schønfeldt, P. M. Smolnicki, R.-M. Soe, J. Sääski, M. Szymańska, I. Vaskinn, and M. Åman, “State of the Art of Automated Buses,” MDPI, 31-Aug-2018. [Online]. Available: https://www.mdpi.com/2071-1050/10/9/3118/htm. [Accessed: 29-Jul-2020].

[6] “Preliminary Report Highway HWY18MH010,” NHTSA, 28-Mar-2018. [Online]. Available: https://www.ntsb.gov/investigations/AccidentReports/Reports/HWY18MH010-prelim.pdf. [Accessed: 26-Jul-2020].

[7] E. Dogan, R. Chatila, S. Chauvier, K. Evans, P. Hadjixenophontos, and J. Perrin, “Ethics in the design of automated vehicles: the AVEthics project,” CEUR Workshop Proceedings, 2016. [Online]. Available: http://ceur-ws.org/Vol-1668/paper2.pdf. [Accessed: 30-Jul-2020].

[8] M. Rowthorn, "How Should Autonomous Vehicles Make Moral Decisions? Machine Ethics, Artificial Driving Intelligence, and Crash Algorithms," Contemporary Readings in Law and Social Justice, vol. 11, (1), pp. 9-14, 2019. Available: http://libproxy.usc.edu/login?url=https://search-proquest-com.libproxy2.usc.edu/docview/2269349615?accountid=14749. DOI: http://dx.doi.org.libproxy2.usc.edu/10.22381/CRLSJ11120191.

[9] D. P. Ljungholm, "The Safety and Reliability of Networked Autonomous Vehicles: Ethical Dilemmas, Liability Litigation Concerns, and Regulatory Issues," Contemporary Readings in Law and Social Justice, vol. 11, (2), pp. 9-14, 2019. Available: http://libproxy.usc.edu/login?url=https://search-proquest-com.libproxy2.usc.edu/docview/2322893910?accountid=14749. DOI: http://dx.doi.org.libproxy2.usc.edu/10.22381/CRLSJ11220191.

Daniel’s references
Adams, E. (2017, June 03). Honda's Making a Self-Balancing Motorcycle, Just for N00bs. Retrieved August 01, 2020, from https://www.wired.com/2017/01/hondas-self-balancing-motorcycle-perfect-noobs/

Boston Dynamics. (n.d.). Retrieved August 01, 2020, from https://www.bostondynamics.com/

Brownell, B. (2020, June 09). Harley-Davidson Wants To Make Self-Balancing Motorcycles By Putting A Gyroscope In Your Top Case. Retrieved August 01, 2020, from https://jalopnik.com/harley-davidson-wants-to-make-self-balancing-motorcycle-1843958686

DATAMARAN AF. (n.d.). Retrieved August 01, 2020, from https://www.automarinesys.com/mark8

Lee, J. (2019, December 23). 3 Companies Looking to Dominate Drone Delivery. Retrieved August 01, 2020, from https://www.fool.com/investing/2019/12/23/3-companies-looking-to-dominate-drone-delivery.aspx

Lindeman, T. (2018). Autonomous Boats Will Be On the Market Sooner Than Self-Driving Cars. Retrieved August 01, 2020, from https://www.vice.com/en_us/article/ne95qm/autonomous-boats-will-be-here-before-self-driving-cars

Nussbaumer, N. (2020, May 12). Self Driving Water Taxis: Buffalo Automation speaks to our Inventive Past. Retrieved August 01, 2020, from https://www.buffalorising.com/2020/05/self-driving-water-taxis-buffalo-automation-speaks-to-our-inventive-past/

Products. (n.d.). Retrieved August 01, 2020, from https://sea-machines.com/products

Self-balancing Yamaha motorcycle comes on command. (2018, December 18). Retrieved August 01, 2020, from https://www.iol.co.za/motoring/bikes/self-balancing-yamaha-motorcycle-comes-on-command-1269783

3

Sorokanich, B. (2020, July 28). Robots Replace Humans the One Place We Least Expected: Motorcycles. Retrieved August 01, 2020, from https://www.roadandtrack.com/new-cars/car-technology/a23083999/bmw-motorrad-self-driving-motorcycle/

Charles’s references
[1] Anderson, M., 2020. "The road ahead for self-driving cars: The AV industry has had to reset expectations, as it shifts its focus to level 4 autonomy - [News]." IEEE Spectrum, 57(5), pp.8-9.

[2]Campbell, M., Egerstedt, M., How, J. and Murray, R., 2010. "Autonomous driving in urban environments: approaches, lessons and challenges." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1928), pp.4649-4672.

[3]Panagiotopoulos, I. and Dimitrakopoulos, G., 2018. "An empirical investigation on consumers’ intentions towards autonomous driving." Transportation Research Part C: Emerging Technologies, 95, pp.773-784.

[4]S. Shladover and C. Nowakowski, "Regulatory challenges for road vehicle automation: Lessons from the California experience", Transportation Research Part A: Policy and Practice, vol. 122, pp. 125-133, 2019. Available: 10.1016/j.tra.2017.10.006 [Accessed 1 August 2020].

Steve’s references
[1] Yigitcanlar, Wilson and Kamruzzaman, "Disruptive Impacts of Automated Driving Systems on the Built Environment and Land Use: An Urban Planner’s Perspective", Journal of Open Innovation: Technology, Market, and Complexity, vol. 5, no. 2, p. 24, 2019. Available: 10.3390/joitmc5020024 [Accessed 14 July 2020].

[2] Adnan, Nadia; Md Nordin, Shahrina; bin Bahruddin, Mohamad Ariff; Ali, Murad (2018-12). "How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle". Transportation Research Part A: Policy and Practice. 118: 819–836. doi:10.1016/j.tra.2018.10.019.

[3] Van Brummelen, Jessica; O’Brien, Marie; Gruyer, Dominique; Najjaran, Homayoun (2018-04). "Autonomous vehicle perception: The technology of today and tomorrow". Transportation Research Part C: Emerging Technologies. 89: 384–406. doi:10.1016/j.trc.2018.02.012.

[4] Bansal, Prateek; Kockelman, Kara M. (2017-01). "Forecasting Americans' long-term adoption of connected and autonomous vehicle technologies". Transportation Research Part A: Policy and Practice. 95: 49–63. doi:10.1016/j.tra.2016.10.013.

[5]  Wigley, Edward; Rose, Gillian (2020-04-02). "Who's behind the wheel? Visioning the future users and urban contexts of connected and autonomous vehicle technologies". Geografiska Annaler: Series B, Human Geography. 102 (2): 155–171. doi:10.1080/04353684.2020.1747943. ISSN 0435-3684.