Intention-Aware Control Based on Belief-Space Specifications and Stochastic Expansion
원문 초록
This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions. We define an intention-aware control problem incorporating epistemic uncertainties of the opponent vehicles and model their intentions as discrete-valued random variables. Then, we focus on a control objective specified as belief-space temporal logic specifications. From this stochastic control problem, we derive a sound deterministic control problem using stochastic expansion and solve it using shrinking-horizon model predictive control. The solved intention-aware controller allows a vehicle to adjust its behaviors according to its opponents' intentions. It ensures provable safety by restricting the probabilistic risk under a desired level. We show with experimental studies that the proposed method ensures strict limitation of risk probabilities, validating its efficacy in autonomous driving cases. This work provides a novel solution for the risk-aware control of interactive vehicles with formal safety guarantees.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control
RNN-based linear parameter varying adaptive model predictive control for autonomous driving
Autonomous driving using GA-optimized neural network based adaptive LPV-MPC controller
Optimized adaptive MPC for lateral control of autonomous vehicles
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.