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Interpretable Collision Avoidance and Feasibility Guarantees through an Uncertainty Aware Pipeline

Summary

This paper presents a modular perception-prediction-planning pipeline that propagates uncertainty as structured, interpretable quantities across modules, decoupling safety guarantees from solver feasibility. In Monte Carlo trials at a four-way unsignalized intersection, the pipeline achieves 0% collisions and 0% solver infeasibility at 12.7 ms per cycle, outperforming deterministic and chance-constrained baselines.

SourcearXiv RoboticsAuthor: Doris Xu, Jin Hyun Park, Guadalupe Alvarado Fernandez, Adhyan Prasad, Mark Campbell
Interpretable Collision Avoidance and Feasibility Guarantees through an Uncertainty Aware Pipeline
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[Submitted on 23 Oct 2026]

Title:Interpretable Collision Avoidance and Feasibility Guarantees through an Uncertainty Aware Pipeline

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Abstract:Autonomous vehicles (AVs) operating in dynamic, challenging conditions such as intersections and roundabouts must guarantee collision avoidance under uncertainty while maintaining real-time feasibility. Most existing planners struggle to simultaneously guarantee both requirements. Chance-constrained formulations embed safety constraints directly in the optimization but become infeasible as uncertainty compounds; deterministic planners provide feasible solutions but fail to address the uncertainty that causes collisions; end-to-end learned policies provide neither guarantee in an interpretable form.

We present a modular perception-prediction-planning pipeline that closes this gap by propagating uncertainty as structured, interpretable quantities across module boundaries. Rather than embedding probabilistic risk directly in the optimization, our pipeline resolves collision probabilities upstream and constructs convex safe sets within which the planner provides a guaranteed feasible solution at every cycle -- decoupling the safety guarantee from solver feasibility.

Across Monte Carlo trials in a four-way unsignalized intersection, our pipeline achieves 0% collisions and 0% solver infeasibility at 12.7ms per cycle, compared to 36% and 10% collision rates for deterministic and chance-constrained baselines respectively. The ablation study confirms the necessity of each uncertainty source and the hardware deployment on Clearpath Jackal UGVs validates the pipeline's real-time viability on physical platforms.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2610.33186 [cs.RO]

(or arXiv:2610.33186v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.33186

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Doris Xu [view email] [v1] Fri, 23 Oct 2026 18:29:09 UTC (588 KB)

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Key points and analysis

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Key points

  • Proposes a modular pipeline that propagates uncertainty as interpretable quantities rather than embedding it in optimization.
  • Achieves 0% collisions and 0% solver infeasibility at 12.7 ms per cycle in a four-way unsignalized intersection.
  • Deterministic and chance-constrained baselines show 36% and 10% collision rates, respectively.
  • Hardware deployment on Clearpath Jackal UGVs validates real-time viability.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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