Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference

08/20/2020
by   Liting Sun, et al.
0

In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Cost/reward learning, as an efficient way to learn and represent human behavior, has been successfully applied in many domains. Most of traditional inverse reinforcement learning (IRL) algorithms, however, cannot adequately capture the diversity of human behavior since they assume that all behavior in a given dataset is generated by a single cost function.In this paper, we propose a probabilistic IRL framework that directly learns a distribution of cost functions in continuous domain. Evaluations on both synthetic data and real human driving data are conducted. Both the quantitative and subjective results show that our proposed framework can better express diverse human driving behaviors, as well as extracting different driving styles that match what human participants interpret in our user study.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

12/11/2018

Guided Exploration of Human Intentions for Human-Robot Interaction

Robot understanding of human intentions is essential for fluid human-rob...
10/28/2020

Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

As more and more autonomous vehicles (AVs) are being deployed on public ...
06/22/2020

Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving

In the past decades, we have witnessed significant progress in the domai...
10/07/2020

Modeling Human Driving Behavior in Highway Scenario using Inverse Reinforcement Learning

Human driving behavior modeling is of great importance for designing saf...
05/20/2021

Quantitative Physical Ergonomics Assessment of Teleoperation Interfaces

Human factors and ergonomics are the essential constituents of teleopera...
07/19/2019

Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory

Understanding human driving behavior is important for autonomous vehicle...
05/02/2019

Behavior Planning of Autonomous Cars with Social Perception

Autonomous cars have to navigate in dynamic environment which can be ful...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.