mlsanityData to 7 Oct 2026

Self-Aware Trajectory Prediction for Safe Autonomous Driving

arXiv 2305.09147 ↗First posted 16 May 2023cs.RO cs.CVcited by 5

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Abstract (arXiv)

Trajectory prediction is one of the key components of the autonomous driving software stack. Accurate prediction for the future movement of surrounding traffic participants is an important prerequisite for ensuring the driving efficiency and safety of intelligent vehicles. Trajectory prediction algorithms based on artificial intelligence have been widely studied and applied in recent years and have achieved remarkable results. However, complex artificial intelligence models are uncertain and difficult to explain, so they may face unintended failures when applied in the real world. In this paper, a self-aware trajectory prediction method is proposed. By introducing a self-awareness module and a two-stage training process, the original trajectory prediction module's performance is estimated online, to facilitate the system to deal with the possible scenario of insufficient prediction function in time, and create conditions for the realization of safe and reliable autonomous driving. Comprehensive experiments and analysis are performed, and the proposed method performed well in terms of self-awareness, memory footprint, and real-time performance, showing that it may serve as a promising paradigm for safe autonomous driving.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

28 of 50 references.

References · 28

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16 more
  1. 2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 112
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Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errorscited by 3
  2. 2024Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasetscited by 2
  3. 2023NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving systemcited by 0
  4. 2024Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Predictioncited by 0
  5. 2025A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systemscited by 0