Risk Assessment Method for Driving Scenarios of Autonomous Vehicles Based on Drivable Area

Wu, Xinzheng; Xing, Xingyu; Chen, Junyi; Shen, Yong; Liu, Xiong

doi:10.1109/itsc55140.2022.9922210

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…By checking the keywords, such as "crossing", the risky semantic relation between the road agents and ego-car can be assessed. Actually, for safe driving, the risk assessment of the driving scenes is very important, which can help to find the risky context in advance [116], [117], [118].…”

Section: Driver Attention Map In An Crossing Accidentmentioning

confidence: 99%

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.

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Section: Driver Attention Map In An Crossing Accidentmentioning

confidence: 99%

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…The reachability analysis can be used to determine the theoretically drivable area of the SUT in a scenario. For example, Wu et al [6] proposed to use the Discretized Normalized Drivable Area (DNDA) as a surrogate for the challenge of a scenario. The DNDA is computed in each scene by dividing the size of the drivable area by the size of the drivable area without any surrounding vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…A current method for determining the challenge of a scenario is to apply safety metrics like the time-to-collision metric to each time step of the scenario and then aggregate the individual metric values to scenario level [4]. Another approach is to use the reachability analysis [5] to determine the theoretically drivable area and use its size as a surrogate for the challenge of the scenario [6]. However, these approaches either require a high manual calibration effort…”

Section: Introductionmentioning

confidence: 99%

Test Case Selection Method for the Verification of Automated Driving Systems

Vater

Pütz

Tellis

et al. 2021

ATZ Electron Worldw

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The selection of relevant test scenarios for the scenario-based testing and safety validation of automated driving systems (ADSs) remains challenging. An important aspect of the relevance of a scenario is the challenge it poses for an ADS. Existing methods for calculating the challenge of a scenario aim to express the challenge in terms of a metric value. Metric values are useful to select the least or most challenging scenario. However, they fail to provide human-interpretable information on the cause of the challenge which is critical information for the efficient selection of relevant test scenarios. Therefore, this paper presents the Challenge Description Method that mitigates this issue by analyzing scenarios and providing a description of their challenge in terms of the minimum required lane changes and their difficulty. Applying the method to different highway scenarios showed that it is capable of analyzing complex scenarios and providing easy-to-understand descriptions that can be used to select relevant test scenarios.

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Accelerated Testing and Evaluation of Autonomous Vehicles Based on Dual Surrogates

Wu,

Xing,

Xiong

et al. 2024

Automot. Innov.

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Risk Assessment Method for Driving Scenarios of Autonomous Vehicles Based on Drivable Area

Cited by 5 publications

References 19 publications

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Test Case Selection Method for the Verification of Automated Driving Systems

Accelerated Testing and Evaluation of Autonomous Vehicles Based on Dual Surrogates

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