Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments
This addresses the challenge of balancing safety and performance in autonomous systems like vehicles, though it is incremental as it builds on existing planning methods with risk constraints.
The paper tackles the problem of planning in probabilistic dynamic environments by developing an algorithm that guarantees bounds on safety violation probability while achieving non-conservative performance, demonstrating improved safety and reduced conservatism in autonomous driving simulations and on a real truck.
Planning in environments with other agents whose future actions are uncertain often requires compromise between safety and performance. Here our goal is to design efficient planning algorithms with guaranteed bounds on the probability of safety violation, which nonetheless achieve non-conservative performance. To quantify a system's risk, we define a natural criterion called interval risk bounds (IRBs), which provide a parametric upper bound on the probability of safety violation over a given time interval or task. We present a novel receding horizon algorithm, and prove that it can satisfy a desired IRB. Our algorithm maintains a dynamic risk budget which constrains the allowable risk at each iteration, and guarantees recursive feasibility by requiring a safe set to be reachable by a contingency plan within the budget. We empirically demonstrate that our algorithm is both safer and less conservative than strong baselines in two simulated autonomous driving experiments in scenarios involving collision avoidance with other vehicles, and additionally demonstrate our algorithm running on an autonomous class 8 truck.