When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving
Overview
This research presents a unified belief-space Model Predictive Control (MPC) framework designed for safe navigation in interactive traffic. The method dynamically co-evolves safety with internal confidence by treating clarity as an explicit planning variable within a Conditional Value-at-Risk (CVaR) barrier mechanism. Evaluated across 5,000 simulations in lane-merging and unsignalized-intersection scenarios, the proposed approach achieves success rates of 94.6% and 93.2% respectively, with collision rates of 1.2% and 1.4%, outperforming representative interactive-planning baselines.
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Environment: simulation
“s showing that the cost of information seeking and the expected planning-cost gap to a controller that knows the true human characteristic are bounded by residual ambiguity and solver error. We evaluate the framework in lane-merging and unsignalized-intersection scenarios against representative interactive-planning baselines. Across 5,000 simulations, the proposed method achieves the highest success rates (94.6% and”
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Control: reported autonomous
“arXiv:2611.02240v1 Announce Type: new Abstract: Safe navigation in interactive traffic requires autonomous vehicles to reason about unobservable human characteristics and the reciprocal nature of multi-agent interactions. Traditional architectures often decouple prediction from planning, leading to the "Frozen Robot Problem" or unsafe maneuvers under high ambiguity because they treat risk assessment and information a”
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Reported quantity · simulations: 5000 other · Basis not reported
“s showing that the cost of information seeking and the expected planning-cost gap to a controller that knows the true human characteristic are bounded by residual ambiguity and solver error. We evaluate the framework in lane-merging and unsignalized-intersection scenarios against representative interactive-planning baselines. Across 5,000 simulations, the proposed method achieves the highest success rates (94.6% and”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 94.6 percent · Basis not reported
“s showing that the cost of information seeking and the expected planning-cost gap to a controller that knows the true human characteristic are bounded by residual ambiguity and solver error. We evaluate the framework in lane-merging and unsignalized-intersection scenarios against representative interactive-planning baselines. Across 5,000 simulations, the proposed method achieves the highest success rates (94.6% and”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalWhen to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving
This research presents a unified belief-space Model Predictive Control (MPC) framework designed for safe navigation in interactive traffic. The method dynamically co-evolves safety with internal confidence by treating clarity as an explicit planning variable within a Conditional Value-at-Risk (CVaR) barrier mechanism. Evaluated across 5,000 simulations in lane-merging and unsignalized-intersection scenarios, the proposed approach achieves success rates of 94.6% and 93.2% respectively, with collision rates of 1.2% and 1.4%, outperforming representative interactive-planning baselines.
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