Learning Driving Decisions by Imitating Drivers' Control Behaviors

11/30/2019
by   Junning Huang, et al.
8

Classical autonomous driving systems are modularized as a pipeline of perception, decision, planning, and control. The driving decision plays a central role in processing the observation from the perception as well as directing the execution of downstream planning and control modules. Commonly the decision module is designed to be rule-based and is difficult to learn from data. Recently end-to-end neural control policy has been proposed to replace this pipeline, given its generalization ability. However, it remains challenging to enforce physical or logical constraints on the decision to ensure driving safety and stability. In this work, we propose a hybrid framework for learning a decision module, which is agnostic to the mechanisms of perception, planning, and control modules. By imitating the low-level control behavior, it learns the high-level driving decisions while bypasses the ambiguous annotation of high-level driving decisions. We demonstrate that the simulation agents with a learned decision module can be generalized to various complex driving scenarios where the rule-based approach fails. Furthermore, it can generate driving behaviors that are smoother and safer than end-to-end neural policies.

READ FULL TEXT

page 2

page 3

page 5

page 6

research
03/24/2023

Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning

Learning-based approaches have achieved impressive performance for auton...
research
12/16/2022

Hippocampus-Inspired Cognitive Architecture (HICA) for Operant Conditioning

The neural implementation of operant conditioning with few trials is unc...
research
01/26/2023

Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing

Although a typical autopilot system far surpasses humans in term of sens...
research
09/28/2018

Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability

Current end-to-end deep learning driving models have two problems: (1) P...
research
01/03/2020

Intelligent Roundabout Insertion using Deep Reinforcement Learning

An important topic in the autonomous driving research is the development...
research
08/02/2022

ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

Existing autonomous driving pipelines separate the perception module fro...
research
05/20/2021

Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

In recent years, we have witnessed increasingly high performance in the ...

Please sign up or login with your details

Forgot password? Click here to reset