SOTIF-Oriented Perception Evaluation Method for Forward Obstacle Detection of Autonomous Vehicles

Chu, Jiayun; Zhao, Tingdi; Jiao, Jian; Yuan, Yuan; Jing, Yongfeng

doi:10.1109/jsyst.2023.3234200

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Our assumptions are listed in Table I. Under A1, the risk induced by the EC making a correct decision (true positive or true negative) is negligible and has already been minimized under safety of the intended functionality [17]. Under A2, the system must generate one of two outputs: 0 or 1; there is no reject option.…”

Section: Problem Definition a Risk Minimization Over Design Parametersmentioning

confidence: 99%

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Yuhas

Easwaran

2022

2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs require heavy utilization of embedded hardware typically found in AVs. For both components, there is a tradeoff between non-functional and functional performance, and both impact a vehicle's safety. For instance, giving an OOD detector a longer response time can increase its accuracy at the expense of the LEC. We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety. We formulate a codesign methodology that uses this risk model to find the design parameters for an OOD detector and LEC that decrease risk below that of the baseline system and demonstrate it on a vision based AEBS. Using our methodology, we achieve a 42.3% risk reduction while maintaining equivalent resource utilization.

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Section: Problem Definition a Risk Minimization Over Design Parametersmentioning

confidence: 99%

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Yuhas

Easwaran

2022

2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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“…These scenarios encompass various aspects, such as functional safety (FS) and the safety of the intended functionality (SOTIF). The presence of perception uncertainty directly affects the engineering practices of AD and represents a key issue that must be addressed in commercial operation [ 2 , 3 ]. Despite the considerable progress in enhancing perception performance in recent years, research on perception uncertainty and its associated risks has not garnered sufficient attention, and effective solutions remain a critical area in need of further exploration.…”

Section: Introductionmentioning

confidence: 99%

Online Quantitative Analysis of Perception Uncertainty Based on High-Definition Map

Yang,

Jiao,

Jiang

et al. 2023

Sensors

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Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships. As a result, the map can provide valuable prior information for assessing the uncertainty associated with static elements. In this paper, a method for evaluating perception uncertainty online, encompassing both static and dynamic elements, is introduced based on the high-definition map. The proposed method is as follows: Firstly, the uncertainty of static elements in perception, including the uncertainty of their existence and spatial information, was assessed based on the spatial and topological features of the static environmental elements; secondly, an online assessment model for the uncertainty of dynamic elements in perception was constructed. The online evaluation of the static element uncertainty was utilized to infer the dynamic element uncertainty, and then a model for recognizing the driving scenario and weather conditions was constructed to identify the triggering factors of uncertainty in real-time perception during autonomous driving operations, which can further optimize the online assessment model for perception uncertainty. The verification results on the nuScenes dataset show that our uncertainty assessment method based on a high-definition map effectively evaluates the real-time perception results’ performance.

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Irregular situations in real-world intelligent systems

Mishra

Kim

2024

Advances in Computers

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SOTIF-Oriented Perception Evaluation Method for Forward Obstacle Detection of Autonomous Vehicles

Cited by 5 publications

References 31 publications

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Online Quantitative Analysis of Perception Uncertainty Based on High-Definition Map

Irregular situations in real-world intelligent systems

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