Accounting for Unpredictability in Autonomous Driving Behaviour

Date

2021-11

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Abstract

Autonomous Vehicles (AVs) need to behave like humans when interacting with them.We define unpredictability of surrounding drivers as a measure to take into account for trajectory planning and use Maximum Entropy Inverse Reinforcement Learning (IRL) to demonstrate that incorporating unpredictability into a lane change reward function provides insights on human driving behaviour. We first evaluate the IRL algorithm on a Linear Quadratic Regulator proof of concept. Then we use the IRL algorithm to model reward functions for conducting a lane change maneuver in a highway setting. We investigate whether the unpredictability of surrounding traffic will have an effect on the behaviour of the lane changing car by learning two reward functions from human data, a baseline reward function and a reward function that incorporates unpredictability. Our evaluation confirms that incorporating unpredictability results in modest improvements in explaining the behaviour of human drivers and can result in human-like AVs.

Description

Keywords

Autonomous Vehicles, Behavior Models, Inverse Optimal Control, Inverse Reinforcement Learning, Motion Planning, Self-driving Cars

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Creative Commons

Attribution-NonCommercial-ShareAlike 4.0 International

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