Collision Avoidance System for Safer Autonomous Vehicle Navigation

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

Authors: Doris Xu, Jin Hyun Park, Guadalupe Alvarado Fernandez, Adhyan Prasad, Mark Campbell

Executive Summary

This research addresses a critical challenge in the development of autonomous vehicles (AVs): ensuring they can avoid collisions while navigating complex environments like intersections and roundabouts. Existing planning methods either compromise on safety or struggle with real-time processing when faced with uncertainties in their operating conditions. This study introduces a new approach that aims to bridge these gaps.

The researchers developed a modular pipeline that integrates perception, prediction, and planning. This system is designed to manage uncertainty more effectively, allowing for safer navigation without sacrificing the speed required for real-time operation. Instead of embedding safety constraints directly into the optimization process—where they often lead to infeasible solutions—the new approach calculates collision probabilities in advance. This allows the system to create defined safe zones within which it can operate confidently.

Testing in a simulated environment of a four-way unsignalized intersection yielded impressive results. The new system recorded a 0% collision rate and 0% solver infeasibility, with each processing cycle taking just 12.7 milliseconds. In contrast, traditional methods—specifically deterministic planners and chance-constrained planners—suffered from collision rates of 36% and 10%, respectively. This stark difference highlights the effectiveness of the proposed pipeline in handling uncertainty while maintaining operational feasibility.

The research also included an ablation study that demonstrated the importance of accounting for various sources of uncertainty in the planning process. This reinforces the robustness of the solution, indicating that each component of the system plays a crucial role in achieving its performance metrics. Furthermore, the system was validated through physical deployment on Clearpath Jackal unmanned ground vehicles (UGVs), confirming its practical applicability in real-world scenarios.

The implications of this research are significant for industries focusing on autonomous systems. By enhancing the safety and reliability of AVs in unpredictable environments, this work could pave the way for broader adoption of autonomous technologies in sectors such as transportation, logistics, and urban planning. The ability to guarantee collision avoidance while operating in real-time enhances trust in AV systems, potentially accelerating their integration into everyday use.

This research demonstrates a promising direction for the future of autonomous vehicle technology, particularly in environments where safety is paramount and uncertainty is high.

Academic 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.