Does Unpredictability Influence Driving Behavior?
On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners
Abstract (arXiv)
In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-car performing a driving maneuver. We use Maximum Entropy Inverse Reinforcement Learning to model reward functions for an ego-car conducting a lane change in a highway setting. We define a new feature based on the unpredictability of surrounding cars and use it in the reward function. We learn two reward functions from human data: a baseline and one that incorporates our defined unpredictability feature, then compare their performance with a quantitative and qualitative evaluation. Our evaluation demonstrates that incorporating the unpredictability feature leads to a better fit of human-generated test data. These results encourage further investigation of the effect of unpredictability on driving behavior.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 20 of its 42 references that are in the map. First posted on arXiv 28 Jul 2023.
Builds on
20 of 42 references.
References · 20
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- 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 176
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- 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 50
- 2016Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networkscited by 46
8 more
- 2017Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labelingcited by 30
- 2018Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networkscited by 24
- 2019Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learningcited by 18
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- 2021On complementing end-to-end human behavior predictors with planningcited by 10
- 2020Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competitioncited by 8
- 2021Dynamically Switching Human Prediction Models for Efficient Planningcited by 2
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- 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning
- 2026Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study
- 2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
- 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
- 2021Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning
- 2019Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
- 2026What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
- 2022Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving