mlsanityData to 14 Oct 2026

Watch out for the risky actors: Assessing risk in dynamic environments for safe driving

arXiv 2110.09998 ↗First posted 19 Oct 2021cs.AI cs.ROcited by 0

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Abstract (arXiv)

Driving in a dynamic environment that consists of other actors is inherently a risky task as each actor influences the driving decision and may significantly limit the number of choices in terms of navigation and safety plan. The risk encountered by the Ego actor depends on the driving scenario and the uncertainty associated with predicting the future trajectories of the other actors in the driving scenario. However, not all objects pose a similar risk. Depending on the object's type, trajectory, position, and the associated uncertainty with these quantities; some objects pose a much higher risk than others. The higher the risk associated with an actor, the more attention must be directed towards that actor in terms of resources and safety planning. In this paper, we propose a novel risk metric to calculate the importance of each actor in the world and demonstrate its usefulness through a case study.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 19 Oct 2021.

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5 of 11 references.

References · 5

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2020Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?cited by 139
  4. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 131
  5. 2020Learning to Evaluate Perception Models Using Planner-Centric Metricscited by 49

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