# Does Unpredictability Influence Driving Behavior?

arXiv: https://arxiv.org/abs/2307.15287 · HTML: https://mlsanity.com/paper/2307.15287/

## 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.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 20 of 20)

- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Stochastic Variational Video Prediction](https://mlsanity.com/paper/1710.11252/) (2017)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Learning to Generate Long-term Future via Hierarchical Prediction](https://mlsanity.com/paper/1704.05831/) (2017)
- [Urban Driving with Conditional Imitation Learning](https://mlsanity.com/paper/1912.00177/) (2019)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks](https://mlsanity.com/paper/1607.02586/) (2016)
- [Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling](https://mlsanity.com/paper/1705.08781/) (2017)
- [Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks](https://mlsanity.com/paper/1812.09395/) (2018)
- [Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning](https://mlsanity.com/paper/1904.12374/) (2019)
- [Learning a Decision Module by Imitating Driver's Control Behaviors](https://mlsanity.com/paper/1912.00191/) (2019)
- [Courteous Autonomous Cars](https://mlsanity.com/paper/1808.02633/) (2018)
- [On complementing end-to-end human behavior predictors with planning](https://mlsanity.com/paper/2103.05661/) (2021)
- [Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competition](https://mlsanity.com/paper/2004.08752/) (2020)
- [Dynamically Switching Human Prediction Models for Efficient Planning](https://mlsanity.com/paper/2103.07815/) (2021)

## 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.