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

TL;DR

This paper introduces a new risk metric for evaluating the importance of actors in dynamic driving environments, helping improve safety planning by focusing on high-risk objects.

Contribution

The paper proposes a novel risk metric that assesses the importance of each actor based on their potential threat, enhancing decision-making in autonomous driving.

Findings

The risk metric effectively identifies high-risk actors in simulated scenarios.

Prioritizing high-risk actors improves safety and decision accuracy.

The case study demonstrates the metric's practical utility in real-world driving situations.

Abstract

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.