Traffic

Additional advantages could include higher speed limits; smoother rides; and increased roadway capacity; and minimized traffic congestion, due to decreased need for safety gaps and higher speeds. Currently, maximum controlled-access highway throughput or capacity according to the U.S. Highway Capacity Manual is about 2,200 passenger vehicles per hour per lane, with about 5% of the available road space is taken up by cars. One study estimated that automated cars could increase capacity by 273% (~8,200 cars per hour per lane). The study also estimated that with 100% connected vehicles using vehicle-to-vehicle communication, capacity could reach 12,000 passenger vehicles per hour (up 445% from 2,200 pc/h per lane) traveling safely at 120 km/h (75 mph) with a following gap of about 6 m (20 ft) of each other. Currently, at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away from the car in front. These increases in highway capacity could have a significant impact in traffic congestion, particularly in urban areas, and even effectively end highway congestion in some places. The ability for authorities to manage traffic flow would increase, given the extra data and driving behavior predictability combined with less need for traffic police and even road signage.

Lower costs

Safer driving is expected to reduce the costs of vehicle insurance. Reduced traffic congestion and the improvements in traffic flow due to widespread use of automated cars will also translate into better fuel efficiency. Additionally, self-driving cars will be able to accelerate and brake more efficiently, meaning higher
fuel economy from reducing wasted energy typically associated with inefficient changes to speed (energy typically lost due to friction, in the form of heat and sound).

Parking space

Manually driven vehicles are reported to be used only 4-5% time, and being parked and unused for the remaining 95-96% of the time. Autonomous vehicles could, on the other hand, be used continuously after it has reached its destination. This could dramatically reduce the need for parking space. For example, in Los Angeles, 14% of the land is used for parking alone, equivalent to some 17,020,594 square meters. This combined with the potential reduced need for road space due to improved traffic flow, could free up tremendous amounts of land in urban areas, which could then be used for parks, recreational areas, buildings, among other uses; making cities more livable.

Related effects

By reducing the (labor and other) cost of mobility as a service, automated cars could reduce the number of cars that are individually owned, replaced by taxi/pooling and other car sharing services. This would also dramatically reduce the size of the automotive production industry, with corresponding environmental and economic effects. Assuming the increased efficiency is not fully offset by increases in demand, more efficient traffic flow could free roadway space for other uses such as better support for pedestrians and cyclists.
The vehicles’ increased awareness could aid the police by reporting on illegal passenger behavior, while possibly enabling other crimes, such as deliberately crashing into another vehicle or a pedestrian. However, this may also lead to much expanded mass surveillance if there is wide access granted to third parties to the large data sets generated. The future of passenger rail transport in the era of automated cars is not clear.

Potential limits or obstacles

The sort of hoped-for potential benefits from increased vehicle automation described may be limited by foreseeable challenges, such as disputes over liability (will each company operating a vehicle accept that they are its “driver” and thus responsible for what their car does, or will some try to project this liability onto others who are not in control?), the time needed to turn over the existing stock of vehicles from non-automated to automated, and thus a long period of humans and autonomous vehicles sharing the roads, resistance by individuals to having to forfeit control of their cars, concerns about the safety of driverless in practice, and the implementation of a legal framework and consistent global government regulations for self-driving cars. Other obstacles could include de-skilling and lower levels of driver experience for dealing with potentially dangerous situations and anomalies, ethical problems where an automated vehicle’s software is forced during an unavoidable crash to choose between multiple harmful courses of action (‘the trolley problem’), concerns about making large numbers of people currently employed as drivers unemployed (at the same time as many other alternate blue collar occupations may be undermined by automation), the potential for more intrusive mass surveillance of location, association and travel as a result of police and intelligence agency access to large data sets generated by sensors and pattern-recognition AI (making anonymous travel difficult), and possibly insufficient understanding of verbal sounds, gestures and non-verbal cues by police, other drivers or pedestrians.
Possible technological obstacles for automated cars are:

Potential disadvantages

A direct impact of widespread adoption of automated vehicles is the loss of driving-related jobs in the road transport industry. There could be resistance from professional drivers and unions who are threatened by job losses. In addition, there could be job losses in public transit services and crash repair shops. The automobile insurance industry might suffer as the technology makes certain aspects of these occupations obsolete. A frequently cited paper by Michael Osborne and Carl Benedikt Frey found that automated cars would make many jobs redundant.
Privacy could be an issue when having the vehicle’s location and position integrated into an interface in which other people have access to. In addition, there is the risk of automotive hacking through the sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols. There is also the risk of terrorist attacks. Self-driving cars could potentially be loaded with explosives and used as bombs.
The lack of stressful driving, more productive time during the trip, and the potential savings in travel time and cost could become an incentive to live far away from cities, where land is cheaper, and work in the city’s core, thus increasing travel distances and inducing more urban sprawl, more fuel consumption and an increase in the carbon footprint of urban travel. There is also the risk that traffic congestion might increase, rather than decrease. Appropriate public policies and regulations, such as zoning, pricing, and urban design are required to avoid the negative impacts of increased suburbanization and longer distance travel.
Some believe that once automation in vehicles reaches higher levels and becomes reliable, drivers will pay less attention to the road. Research shows that drivers in automated cars react later when they have to intervene in a critical situation, compared to if they were driving manually. Depending on the capabilities of automated vehicles and the frequency with which human intervention is needed, this may counteract any increase in safety, as compared to all-human driving, that may be delivered by other factors.
Ethical and moral reasoning come into consideration when programming the software that decides what action the car takes in an unavoidable crash; whether the automated car will crash into a bus, potentially killing people inside; or swerve elsewhere, potentially killing its own passengers or nearby pedestrians. A question that programmers of AI systems find difficult to answer (as do ordinary people, and ethicists) is “what decision should the car make that causes the ‘smallest’ damage to people’s lives?”
The ethics of automated vehicles are still being articulated, and may lead to controversy. They may also require closer consideration of the variability, context-dependency, complexity and non-deterministic nature of human ethics. Different human drivers make various ethical decisions when driving, such as avoiding harm to themselves, or putting themselves at risk to protect others. These decisions range from rare extremes
such as self-sacrifice or criminal negligence, to routine decisions good enough to keep the traffic flowing but bad enough to cause accidents, road rage and stress.
Human thought and reaction time may sometimes be too slow to detect the risk of an upcoming fatal crash, think through the ethical implications of the available options, or take an action to implement an ethical choice. Whether a particular automated vehicle’s capacity to correctly detect an upcoming risk, analyze the options or choose a ‘good’ option from among bad choices would be as good or better than a particular human’s may be difficult to predict or assess. This difficulty may be in part because the level of automated vehicle system understanding of the ethical issues at play in a given road scenario, sensed for an instant from out of a continuous stream of synthetic physical predictions of the near future, and dependent on layers of pattern recognition and situational intelligence, may be opaque to human inspection because of its origins in probabilistic machine learning rather than a simple, plain English ‘human values’ logic of parsable rules. The depth of understanding, predictive power and ethical sophistication needed will be hard to implement, and even harder to test or assess.
The scale of this challenge may have other effects. There may be few entities able to marshal the resources and AI capacity necessary to meet it, as well as the capital necessary to take an automated vehicle system to market and sustain it operationally for the life of a vehicle, and the legal and ‘government affairs’ capacity to deal with the potential for liability for a significant proportion of traffic accidents. This may have the effect of narrowing the number of different system operators, and eroding the presently quite diverse global vehicle market down to a small number of system suppliers.