DeepAI AI Chat
Log In Sign Up

Closing the Planning-Learning Loop with Application to Autonomous Driving in a Crowd

01/11/2021
by   Panpan Cai, et al.
0

Imagine an autonomous robot vehicle driving in dense, possibly unregulated urban traffic. To contend with an uncertain, interactive environment with many traffic participants, the robot vehicle has to perform long-term planning in order to drive effectively and approach human-level performance. Planning explicitly over a long time horizon, however, incurs prohibitive computational cost and is impractical under real-time constraints. To achieve real-time performance for large-scale planning, this paper introduces Learning from Tree Search for Driving (LeTS-Drive), which integrates planning and learning in a close loop. LeTS-Drive learns a driving policy from a planner based on sparsely-sampled tree search. It then guides online planning using this learned policy for real-time vehicle control. These two steps are repeated to form a close loop so that the planner and the learner inform each other and both improve in synchrony. The entire algorithm evolves on its own in a self-supervised manner, without explicit human efforts on data labeling. We applied LeTS-Drive to autonomous driving in crowded urban environments in simulation. Experimental results clearly show that LeTS-Drive outperforms either planning or learning alone, as well as open-loop integration of planning and learning.

READ FULL TEXT

page 1

page 8

05/29/2019

LeTS-Drive: Driving in a Crowd by Learning from Tree Search

Autonomous driving in a crowded environment, e.g., a busy traffic inters...
11/11/2020

Simulating Autonomous Driving in Massive Mixed Urban Traffic

Autonomous driving in an unregulated urban crowd is an outstanding chall...
11/11/2019

SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic

Autonomous driving in an unregulated urban crowd is an outstanding chall...
06/22/2021

nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

In this work, we propose the world's first closed-loop ML-based planning...
09/26/2021

Anytime Game-Theoretic Planning with Active Reasoning About Humans' Latent States for Human-Centered Robots

A human-centered robot needs to reason about the cognitive limitation an...
09/23/2022

LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty

Uncertainty on human behaviors poses a significant challenge to autonomo...
05/02/2017

Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

Each year, millions of motor vehicle traffic accidents all over the worl...