都市自動運転のためのオンラインMPCの参照を学習する
Learning the References of Online Model Predictive Control for Urban Self-Driving
深層強化学習で生センサデータからオンラインMPCの瞬時参照とコスト関数を学習し、都市自動運転の運動計画・制御を実現するフレームワークを提案。
著者: Yubin Wang, Zengqi Peng, Yusen Xie, Yulin Li, Hakim Ghazzai, Jun Ma
分類: cs.RO
原文アブストラクト
In this work, we propose a novel learning-based model predictive control (MPC) framework for motion planning and control of urban self-driving. In this framework, instantaneous references and cost functions of online MPC are learned from raw sensor data without relying on any oracle or predicted states of traffic. Moreover, driving safety conditions are latently encoded via the introduction of a learnable instantaneous reference vector. In particular, we implement a deep reinforcement learning (DRL) framework for policy search, where practical and lightweight raw observations are processed to reason about the traffic and provide the online MPC with instantaneous references. The proposed approach is validated in a high-fidelity simulator, where our development manifests remarkable adaptiveness to complex and dynamic traffic. Furthermore, sim-to-real deployments are also conducted to evaluate the generalizability of the proposed framework in various real-world applications. Also, we provide the open-source code and video demonstrations at the project website: https://latent-mpc.github.io/.
