Simulation-Based Reduction of Operational and Cybersecurity Risks in Autonomous Vehicles

When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

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Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

Journal of Advanced Transportation ◽

10.1155/2018/6135183 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 24

Author(s):

Mark Mario Morando ◽

Qingyun Tian ◽

Long T. Truong ◽

Hai L. Vu

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Assessment Model ◽

Traffic Crashes ◽

Safety Measures ◽

High Penetration ◽

Simulation Based ◽

Car Following Model ◽

Surrogate Safety Measures ◽

Signalised Intersection

Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).

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Framework for simulation-based lane change control for autonomous vehicles

2017 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2017.7995799 ◽

2017 ◽

Cited By ~ 1

Author(s):

Seongjin Choi ◽

Hwasoo Yeo

Keyword(s):

Autonomous Vehicles ◽

Lane Change ◽

Simulation Based

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A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12716 ◽

2021 ◽

Vol 72 ◽

Author(s):

Anthony Corso ◽

Robert Moss ◽

Mark Koren ◽

Ritchie Lee ◽

Mykel Kochenderfer

Keyword(s):

Autonomous Vehicles ◽

Cyber Physical Systems ◽

Black Box ◽

State Spaces ◽

Physical Systems ◽

Safety Critical ◽

Simulated Environment ◽

Rigorous Testing ◽

Simulation Based ◽

Sampling Problem

Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of formal verification and real-world testing can be too dangerous during development. Therefore, simulation-based techniques have been developed that treat the system under test as a black box operating in a simulated environment. Safety validation tasks include finding disturbances in the environment that cause the system to fail (falsification), finding the most-likely failure, and estimating the probability that the system fails. Motivated by the prevalence of safety-critical artificial intelligence, this work provides a survey of state-of-the-art safety validation techniques for CPS with a focus on applied algorithms and their modifications for the safety validation problem. We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling. Problem decomposition techniques are presented to help scale algorithms to large state spaces, which are common for CPS. A brief overview of safety-critical applications is given, including autonomous vehicles and aircraft collision avoidance systems. Finally, we present a survey of existing academic and commercially available safety validation tools.

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