Collision Avoidance

Collision avoidance studies how to detect potential impacts and choose safe actions for systems ranging from road vehicles and drones to robots, ships, and spacecraft. Common approaches include geometric methods such as velocity obstacles, model predictive control, control barrier functions, and reachability analysis, alongside reinforcement learning and learned perception. Work increasingly accounts for uncertainty in motion, sensing, communication, and hidden obstacles, and tests methods in dense multi-agent settings as well as real-world deployments. A central challenge is balancing strong safety guarantees with efficient, timely decisions that are not so conservative that they block useful motion.