Learning the References of Online Model Predictive Control for Urban Self-Driving

TL;DR

This paper introduces a learning-based MPC framework for urban self-driving that learns references directly from raw sensor data using deep reinforcement learning, enhancing adaptability and safety in complex traffic environments.

Contribution

It presents a novel approach combining deep reinforcement learning with MPC, learning instantaneous references without relying on traffic predictions or oracle data.

Findings

Demonstrates high adaptability in complex traffic scenarios

Validates effectiveness through high-fidelity simulation

Shows promising generalization in real-world deployments

Abstract

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/.