Assistive Decision-Making for Right of Way Navigation at Uncontrolled Intersections

• URL: http://arxiv.org/abs/2509.18407v1
• Date: Mon, 22 Sep 2025 20:46:23 GMT
• Title: Assistive Decision-Making for Right of Way Navigation at Uncontrolled Intersections
• Authors: Navya Tiwari, Joseph Vazhaeparampil, Victoria Preston,
• Abstract summary: Uncontrolled intersections account for a significant fraction of roadway crashes due to ambiguous right-of-way rules.<n>We present a driver-assist framework for right-of-way reasoning at uncontrolled intersections, formulated as a Partially Observable Markov Decision Process.<n>Probability planners outperform the rule-based baseline, achieving up to 97.5 percent collision-free navigation under partial observability.
• Score: 0.17205106391379021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncontrolled intersections account for a significant fraction of roadway crashes due to ambiguous right-of-way rules, occlusions, and unpredictable driver behavior. While autonomous vehicle research has explored uncertainty-aware decision making, few systems exist to retrofit human-operated vehicles with assistive navigation support. We present a driver-assist framework for right-of-way reasoning at uncontrolled intersections, formulated as a Partially Observable Markov Decision Process (POMDP). Using a custom simulation testbed with stochastic traffic agents, pedestrians, occlusions, and adversarial scenarios, we evaluate four decision-making approaches: a deterministic finite state machine (FSM), and three probabilistic planners: QMDP, POMCP, and DESPOT. Results show that probabilistic planners outperform the rule-based baseline, achieving up to 97.5 percent collision-free navigation under partial observability, with POMCP prioritizing safety and DESPOT balancing efficiency and runtime feasibility. Our findings highlight the importance of uncertainty-aware planning for driver assistance and motivate future integration of sensor fusion and environment perception modules for real-time deployment in realistic traffic environments.

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