Imitating Driver Behavior with Generative Adversarial Networks

2017-01-24 · Alex Kuefler, Jeremy Morton, Tim Wheeler, Mykel Kochenderfer

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.

📄 PDF Abstract BibTeX arXiv:1701.06699

Code (1)

sisl/gail-driver 공식 구현

Tasks

Imitation Learning

Error Bounds of Imitating Policies and Environments

2020-10-22 · NeurIPS 2020 12 · Tian Xu, Ziniu Li, Yang Yu

Imitation learning trains a policy by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understanding needs further studies. In this paper, w…

Imitation Learning Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior

2017-04-19 · Jeremy Morton, Mykel J. Kochenderfer

In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectori…

A Probabilistic Framework for Imitating Human Race Driver Behavior

2020-01-22 · Stefan Löckel, Jan Peters, Peter van Vliet

Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existi…

Car Racing Imitation Learning

Generative Adversarial Imitation Learning

2016-06-10 · NeurIPS 2016 12 · Jonathan Ho, Stefano Ermon

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, t…

Imitation Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

2020-05-19 · Cong Fei, Bin Wang, Yuzheng Zhuang, Zongzhang Zhang 외

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single moda…

Autonomous Vehicles Data Augmentation Imitation Learning