Noah J. Goodall

Ethical Decision Making during Automated Vehicle Crashes

Automated vehicles have received much attention recently, particularly the Defense Advanced Research Projects Agency Urban Challenge vehicles, Googles self-driving cars, and various others from auto manufacturers. These vehicles have the potential to reduce crashes and improve roadway efficiency significantly by automating the responsibilities of the driver. Still, automated vehicles are expected to crash occasionally, even when all sensors, vehicle control components, and algorithms function perfectly. If a human driver is unable to take control in time, a computer will be responsible for precrash behavior. Unlike other automated vehicles, such as aircraft, in which every collision is catastrophic, and unlike guided track systems, which can avoid collisions only in one dimension, automated roadway vehicles can predict various crash trajectory alternatives and select a path with the lowest damage or likelihood of collision. In some situations, the preferred path may be ambiguous. The study reported here investigated automated vehicle crashing and concluded the following: (a) automated vehicles would almost certainly crash, (b) an automated vehicles decisions that preceded certain crashes had a moral component, and (c) there was no obvious way to encode complex human morals effectively in software. The paper presents a three-phase approach to develop ethical crashing algorithms; the approach consists of a rational approach, an artificial intelligence approach, and a natural language requirement. The phases are theoretical and should be implemented as the technology becomes available.

Machine Ethics and Automated Vehicles

Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver’s oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research.

Microscopic Estimation of Freeway Vehicle Positions from the Behavior of Connected Vehicles

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicles acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

Microscopic Estimation of Arterial Vehicle Positions in a Low-Penetration-Rate Connected Vehicle Environment

AbstractWireless communication among vehicles and roadside infrastructure, known as connected vehicles, is expected to provide higher-resolution real-time vehicle data, which will allow more effective traffic monitoring and control. Availability of connected vehicle technology among the vehicle fleet will likely grow gradually, but it will possibly remain limited, with many drivers potentially being unwilling to transmit their locations. This is problematic given that research has indicated that the effectiveness of many connected vehicle mobility applications will depend on the availability of location data from a minimum of 20–30% of roadway vehicles. In an effort to improve the performance of connected vehicle applications at low connected vehicle technology penetration rates, the authors propose a novel technique to estimate the positions of noncommunicating (unequipped) vehicles based on the behaviors of communicating (equipped) vehicles along a signalized arterial. Unequipped vehicle positions are e...

Away from Trolley Problems and Toward Risk Management

ABSTRACT As automated vehicles receive more attention from the media, there has been an equivalent increase in the coverage of the ethical choices a vehicle may be forced to make in certain crash situations with no clear safe outcome. Much of this coverage has focused on a philosophical thought experiment known as the “trolley problem,” and substituting an automated vehicle for the trolley and the car’s software for the bystander. While this is a stark and straightforward example of ethical decision making for an automated vehicle, it risks marginalizing the entire field if it is to become the only ethical problem in the public’s mind. In this chapter, I discuss the shortcomings of the trolley problem, and introduce more nuanced examples that involve crash risk and uncertainty. Risk management is introduced as an alternative approach, and its ethical dimensions are discussed.

What Drives Decisions of Single-Occupant Travelers in High-Occupancy Vehicle Lanes?: Investigation Using Archived Traffic and Tolling Data from MnPASS Express Lanes

High-occupancy toll (HOT) lanes are in operation, under construction, and planned in several major metropolitan areas. The premise behind HOT lanes is to allow single-occupant vehicles (SOVs) to access high-occupancy vehicle lanes (and a higher level of service) if they are willing to pay a toll. To maintain a high level of service in the HOT lanes, the toll rate is set dynamically to restrict the number of SOVs that access the facility lanes as they near capacity. Thus, HOT facilities provide operators of transportation systems with an additional tool: pricing. To use pricing effectively, it is critical for those operators to understand how drivers behave when faced with a set of traffic conditions and toll levels. This paper presents the results of an empirical investigation into the relationship between toll rate, traffic conditions, and SOV driver behavior, on the basis of data from the dynamically tolled I-394 HOT facility in Minneapolis, Minnesota. Analysis of the empirical data indicated that a large percentage of SOV drivers used the HOT lanes at different, yet predictable, rates throughout the morning peak period, even when there was no clear travel time advantage. After these users were accounted for, it was determined that the remaining SOV drivers used the HOT lanes at greater rates when the cost per hour of commute time saved was lowest. A model was developed that incorporates both of these findings, predicting HOT lane usage rates based on time savings, time of day, and toll rates, with an R2 value of .684.

Probability of Secondary Crash Occurrence on Freeways with the Use of Private-Sector Speed Data

A percentage of crashes on freeways are suspected to be caused in part by congestion or distraction from earlier incidents. Identification and prevention of these secondary crashes are major goals of transportation agencies, yet the characteristics of secondary crashes—in particular the probability of their occurrence—are poorly understood. Many secondary crashes occur when a vehicle encounters nonrecurring congestion, yet previous efforts to identify incident queues and their secondary crashes have relied either on deterministic queuing theory or on data from uniformly spaced dense loop detectors. This study is the first analysis of secondary crash occurrence to integrate incident timelines and traffic volumes with widely available and legally obtained private-sector speed data. Analysis found that 9.2% of all vehicle crashes were secondary to another incident and that 6.2% of these crashes were tertiary to another primary incident. Secondary crashes occurred on average once every 10 crashes and 54 disabled vehicles. The findings support a fast incident response, because the probability of a secondary crash occurrence increases approximately 1 percentage point for every additional 2 to 3 min spent on the scene in high-volume scenarios.

From Trolleys to Risk: Models for Ethical Autonomous Driving

An introduction is presented in which the author discusses an article in the issue which deals with the public health implications of autonomous vehicles and the ethical aspects of autonomous driving, and it mentions AV accidents, risk management, and a trolley problem thought experiment.

How to Think About Driverless Vehicles

It is difficult to overstate how driverless transportation will affect the economy, public health, and personal mobility. The numbers are staggering—87% cheaper cab fares, 39% reduction in trucking costs saving the industry

Traffic Signal Control with Connected Vehicles

500 billion each year in the United States, up to 1.3 million lives saved from crashes worldwide—and it will allow frictionless movement for the young, elderly, and mobility impaired.