Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Sun, Haitian; Feng, Shuo; Yan, Xintao; Liu, Henry

doi:10.48550/arxiv.2102.03483

Cited by 2 publications

(2 citation statements)

References 42 publications

(50 reference statements)

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“…Since the AV and objects in scenarios interact step-wise, this problem can be formulated in an RL framework, and there are lots of works using RL methods. [66] and [67] use DQN to generate discrete adversarial traffic scenarios. [58] uses A2C [115] to control one surrounding vehicle in the car following scenarios.…”

Section: Adversarial Policymentioning

confidence: 99%

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Ding¹,

Xu²,

Arief³

et al. 2022

Preprint

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Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment in the real world is their safety evaluation. Most existing driving systems are still trained and evaluated on naturalistic scenarios collected from daily life or heuristically-generated adversarial ones. However, the large population of cars, in general, leads to an extremely low collision rate, indicating that the safety-critical scenarios are rare in the collected real-world data. Thus, methods to artificially generate scenarios become crucial to measure the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation in autonomous driving. We first provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to five main challenges of current works -fidelity, efficiency, diversity, transferability, controllability -and research opportunities lighted up by these challenges.

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Section: Adversarial Policymentioning

confidence: 99%

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Ding¹,

Xu²,

Arief³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…ChauffeurNet [33] elaborates on the idea of imitation learning for training robust autonomous agents that leverages worst case scenarios in the form of realistic perturbations. A clustering based collision case generation study [34] systematically defines and generates the different type of collisions for effectively identifying valuable cases for agent training.…”

Section: Previous Workmentioning

confidence: 99%

Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

Rana,

Malhi

2021

Preprint

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Simulation environments are good for learning different driving tasks like lane changing, parking or handling intersections etc. in an abstract manner. However, these simulation environments often restrict themselves to operate under conservative interactions behavior amongst different vehicles. But, as we know that the real driving tasks often involves very high risk scenarios where other drivers often don't behave in the expected sense. There can be many reasons for this behavior like being tired or inexperienced. The simulation environments doesn't take this information into account while training the navigation agent. Therefore, in this study we especially focus on systematically creating these risk prone scenarios with heavy traffic and unexpected random behavior for creating better model-free learning agents. We generate multiple autonomous driving scenarios by creating new custom Markov Decision Process (MDP) environment iterations in highway-env simulation package. The behavior policy is learnt by agents trained with the help from deep reinforcement learning models. Our behavior policy is deliberated to handle collisions and risky randomized driver behavior. We train model free learning agents with supplement information of risk prone driving scenarios and compare their performance with baseline agents. Finally, we casually measure the impact of adding these perturbations in the training process to precisely account for the performance improvement attained from utilizing the learnings from these scenarios.

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Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Cited by 2 publications

References 42 publications

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

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