A Risk-index based Sampling Method to Generate Scenarios for the Evaluation of Automated Driving Vehicle Safety

Akagi, Yasuhiro; Kato, Ryosuke; Kitajima, Sou; Antona‐Makoshi, Jacobo; Uchida, Noriki

doi:10.1109/itsc.2019.8917311

Cited by 36 publications

(35 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An accelerated method without making a quantitative statement about the overall risk level is presented by [107]. Analogous to the accelerated methods, the parameter distributions are determined on the basis of real data.…”

Section: B Sampling From Parameter Distributionsmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

View full text Add to dashboard Cite

When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

show abstract

Section: B Sampling From Parameter Distributionsmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

View full text Add to dashboard Cite

show abstract

“…To further address the challenge brought by the high dimensionality of complex environments (e.g., highway driving), Feng et al (12) proposed a framework of generating a naturalistic and adversarial driving environment by adding sparse but adversarial adjustments to the NDE. Akagi et al (42) use a self-defined risky index and NDD to sample critical cut-in scenarios. O'Kelly et al (43) utilized neural network and imitation learning to calibrate the naturalistic driving model from the nextgeneration simulation data, and then chose a highway with six vehicles to generate testing cases.…”

Section: Corner Case Generation For Vehicle Decision-makingmentioning

confidence: 99%

Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Sun

Feng

Yan

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

Testing and evaluation is a crucial step in the development and deployment of connected and automated vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is necessary to test the CAVs in safety-critical scenarios, which rarely happen in a naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put into decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high-dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on the Markov decision process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.

show abstract

“…To reduce the overvalue problem of worst cases, Feng et al (12,(38)(39)(40)(41) defined the maneuver challenge and exposure frequency terms and generated cases on various environment settings, including cut-in scenarios, car-following scenarios, and highwaydriving environment. Akagi et al (42) use self-defined risky index and naturalistic driving data to sample critical cut-in scenarios. O'Kelly et al (43) utilized neuron network and imitation learning to calibrate naturalistic driving model from the NGSIM data, and then a highway with 6 vehicles are chosen to generate testing cases.…”

Section: Corner Cases Generation For Vehicle Decision-makingmentioning

confidence: 99%

Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Sun

Feng

Yan

et al. 2021

Preprint

View full text Add to dashboard Cite

Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.

show abstract

A Risk-index based Sampling Method to Generate Scenarios for the Evaluation of Automated Driving Vehicle Safety

Cited by 36 publications

References 12 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Contact Info

Product

Resources

About