When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving
When 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.
The study introduces a unified belief-space Model Predictive Control framework that dynamically couples information planning with safety constraints, evaluated across 5,000 simulations in lane-merging and unsignalized-intersection scenarios.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S5
- Control
- Reported autonomousOpen source S2
Reported numbers
simulations
5,000
View original evidence
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
Open source S5Reported success rate
94.6%
View original evidence
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
Open source S5
- paper: not_reported
What remains unknown
Not established in the collected evidence: Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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
Open source S5
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
Open source S2
Source：arXiv Robotics — research abstracts · arxiv.org