Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon.
Investigating Traffic Crashes Involving Autonomous Vehicles
Deaths due to road traffic accidents are one of the leading causes of death in the United States. Furthermore, the economic impact of road traffic accidents accounts for about 3% of the United States' annual gross domestic product (GDP). In the past decade, extensive research has focused on autonomous vehicles (AVs). This technology is said to help prevent traffic accidents while promoting road traffic safety. This study aims to investigate the safety performance of AVs and identify the significant risk factors associated with the AV collisions. The study considers more than 200 crashes involving AVs and includes vehicle factors, environmental factors, collision type and crash severity. Multinomial logistic regression was conducted with collision type. The results showed no statistically significant risk factors to crash severity. However, movement preceding to collision contributes significantly to collision type. When both vehicles are moving, there's a higher likelihood of an angled collision, 47% to be exact. The other scenario which demonstrates a high probability of an angled collision is when the AV vehicle is not moving while the other is moving. The highest probability for a rear-end collision to occur is when the AV vehicle is not moving while the other is moving. This scenario makes up 55% of the entire rear-end collisions. As for the second-highest proportion, both vehicles moving, it consists of 42%. The research shall help reduce AV involved collisions and increase driving safety.
more »
« less
- Award ID(s):
- 1726500
- NSF-PAR ID:
- 10311046
- Editor(s):
- A. Ghate, K. Krishnaiyer
- Date Published:
- Journal Name:
- IISE Annual Conference Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
Despite the potential of autonomous vehicles (AV) to improve traffic efficiency and safety, many studies have shown that traffic accidents in a hybrid traffic environment where both AV and human-driven vehicles (HVs) are present are inevitable because of the unpredictability of HVs. Given that eliminating accidents is impossible, an achievable goal is to design AVs in a way so that they will not be blamed for any accident in which they are involved in. In this paper, we propose BlaFT Rules – or Blame-Free hybrid Traffic motion planning Rules. An AV following BlaFT Rules is designed to be cooperative with HVs as well as other AVs, and will not be blamed for accidents in a structured road environment. We provide proofs that no accidents will happen if all AVs are using a BlaFT Rules conforming motion planner, and that an AV using BlaFT Rules will be blame-free even if it is involved in a collision in hybrid traffic. We implemented a motion planning algorithm that conforms to BlaFT Rules called BlaFT. We instantiated scores of BlaFT controlled AVs and HVs in an urban roadscape loop in the SUMO simulator and show that over time that as the percentage of BlaFT vehicles increases, the traffic becomes safer even with HVs involved. Adding BlaFT vehicles increases the efficiency of traffic as a whole by up to 34% over HVs alone.more » « less
-
Self-driving autonomous vehicles (AVs) have recently gained popularity as a research topic. The safety of AVs is exceptionally important as failure in the design of an AV could lead to catastrophic consequences. AV systems are highly heterogeneous with many different and complex components, so it is difficult to perform end-to-end testing. One solution to this dilemma is to evaluate AVs using simulated racing competition. In this thesis, we present a simulated autonomous racing competition, Generalized RAcing Intelligence Competition (GRAIC). To compete in GRAIC, participants need to submit their controller files which are deployed on a racing ego-vehicle on different race tracks. To evaluate the submitted controller, we also developed a testing pipeline, Autonomous System Operations (AutOps). AutOps is an automated, scalable, and fair testing pipeline developed using software engineering techniques such as continuous integration, containerization, and serverless computing. In order to evaluate the submitted controller in non-trivial circumstances, we populate the race tracks with scenarios, which are pre-defined traffic situations commonly seen in the real road. We present a dynamic scenario testing strategy that generates new scenarios based on results of the ego-vehicle passing through previous scenarios.more » « less
-
null (Ed.)In this paper we provide a proof of principle of a new method for addressing the ethics of autonomous vehicles (AVs), the Data-Theories Method, in which vehicle crash data is combined with philosophical ethical theory to provide a guide to action for AV algorithm design. We use this method to model three scenarios in which an AV is exposed to risk on the road, and determine possible actions for the AV. We then examine how different philosophical perspectives on agent partiality, or the degree to which one can act in one’s own self-interest, might address each scenario. This method shows why modelling the ethics of AVs using data is essential. First, AVs may sometimes have options that human drivers do not, and designing AVs to mimic the most ethical human driver would not ensure that they do the right thing. Second, while ethical theories can often disagree about what should be done, disagreement can be reduced and compromises found with a more complete understanding of the AV’s choices and their consequences. Finally, framing problems around thought experiments may elicit preferences that are divergent with what individuals might prefer once they are provided with information about the real risks for a scenario. Our method provides a principled and empirical approach to productively address these problems and offers guidance on AV algorithm design.more » « less
-
Autonomous vehicle (AV) technology is a huge leap forward in capability for mobility. To be effective, the current human based vehicle safety infrastructure will have to be upgraded. A critical leg of this infrastructure is the automobile accident report. Conventional vehicle accident reports have evolved to a point where law enforcement have a reasonably standard approach focused on humans. However, with AVs there are no drivers to interview. Also, given their automation, a flaw found in an AV has the potential to be a systemic risk. In this respect, AVs must be handled more like airplanes in terms of post accident procedures. In this paper, we explore the requirements for AV accident reports and the escalation procedures required to avoid systemic risks. Our methodology is to analyze all the information available (crash reports as well as press accounts) of AV accidents to date with a special focus on the fatal accidents. As a result of this work, a recommendation of an AV crash report template, associated escalation procedure, and an infrastructure for accumulated learning is presented.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
