Review of Graph-Based Hazardous Event Detection Methods for Autonomous Driving Systems

Xiao, Dannier; Dianati, Mehrdad; Geiger, William Goncalves; Woodman, Roger

doi:10.1109/tits.2023.3240104

Cited by 18 publications

(2 citation statements)

References 91 publications

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“…However, there might be different levels or types of where, where some might not require a safety response or be mitigated by other measures. Additionally, in certain situations, perception quality is of paramount importance for other safety-relate mechanisms, particularly in the event of a potential accident, hazardous situation [121], [122] or an irregular driver behaviour [123]. These scenarios warrant further examination, which can be achieved through introspection.…”

Section: Open Research Challengesmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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Section: Open Research Challengesmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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“…The acceleration of urbanization has rendered automobiles indispensable for daily commuting, enhancing travel efficiency yet concurrently engendering a host of societal challenges, including escalating road accidents, urban traffic congestion, and environmental degradation [1,2]. According to statistics, 83%-94% of traffic accidents are related to human fault [3]. The advent of autonomous vehicles and the advancement of intelligent network infrastructure have been identified as pivotal strategies to ameliorate these issues.…”

Section: Introductionmentioning

confidence: 99%

An Expository Examination of Temporally Evolving Graph-Based Approaches for the Visual Investigation of Autonomous Driving

Wan,

Cheng

2024

IET Software

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With the continuous advancement of autonomous driving technology, visual analysis techniques have emerged as a prominent research topic. The data generated by autonomous driving is large-scale and time-varying, yet more than existing visual analytics methods are required to deal with such complex data effectively. Time-varying diagrams can be used to model and visualize the dynamic relationships in various complex systems and can visually describe the data trends in autonomous driving systems. To this end, this paper introduces a time-varying graph-based method for visual analysis in autonomous driving. The proposed method employs a graph structure to represent the relative positional relationships between the target and obstacle interferences. By incorporating the time dimension, a time-varying graph model is constructed. The method explores the characteristic changes of nodes in the graph at different time instances, establishing feature expressions that differentiate target and obstacle motion patterns. The analysis demonstrates that the feature vector centrality in the time-varying graph effectively captures the distinctions in motion patterns between targets and obstacles. These features can be utilized for accurate target and obstacle recognition, achieving high recognition accuracy. To evaluate the proposed time-varying graph-based visual analytic autopilot method, a comparative study is conducted against traditional visual analytic methods such as the frame differencing method and advanced visual analytic methods like visual lidar odometry and mapping. Robustness, accuracy, and resource consumption experiments are performed using the publicly available KITTI dataset to analyze and compare the three methods. The experimental results show that the proposed time-varying graph-based method exhibits superior accuracy and robustness. This study offers valuable insights and solution ideas for developing deep integration between intelligent networked vehicles and intelligent transportation. It provides a reference for advancing intelligent transportation systems and their integration with autonomous driving technologies.

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Survey on video anomaly detection in dynamic scenes with moving cameras

Jiao,

Wan,

Poiesi

et al. 2023

Artif Intell Rev

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Review of Graph-Based Hazardous Event Detection Methods for Autonomous Driving Systems

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 18 publications

References 91 publications

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

An Expository Examination of Temporally Evolving Graph-Based Approaches for the Visual Investigation of Autonomous Driving

Survey on video anomaly detection in dynamic scenes with moving cameras

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