Driving in Real Life with Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2206.03004v1
• Date: Tue, 7 Jun 2022 04:36:46 GMT
• Title: Driving in Real Life with Inverse Reinforcement Learning
• Authors: Tung Phan-Minh and Forbes Howington and Ting-Sheng Chu and Sang Uk Lee and Momchil S. Tomov and Nanxiang Li and Caglayan Dicle and Samuel Findler and Francisco Suarez-Ruiz and Robert Beaudoin and Bo Yang and Sammy Omari and Eric M. Wolff
• Abstract summary: We introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL) DriveIRL generates a diverse set of trajectory proposals, filters these with a lightweight and interpretable safety filter, and then uses a learned model to score each remaining trajectory. We validated DriveIRL on the Las Vegas Strip and demonstrated fully autonomous driving in heavy traffic.
• Score: 4.366642479205039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals, filters these trajectories with a lightweight and interpretable safety filter, and then uses a learned model to score each remaining trajectory. The best trajectory is then tracked by the low-level controller of our self-driving vehicle. We train our trajectory scoring model on a 500+ hour real-world dataset of expert driving demonstrations in Las Vegas within the maximum entropy IRL framework. DriveIRL's benefits include: a simple design due to only learning the trajectory scoring function, relatively interpretable features, and strong real-world performance. We validated DriveIRL on the Las Vegas Strip and demonstrated fully autonomous driving in heavy traffic, including scenarios involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff zones. Our dataset will be made public to help further research in this area.

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