Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning
| Autor: | Naoki Akai, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda, Yoichi Morales, Hailong Liu, Kyle Sama |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Deep learning Autonomous agent Aerospace Engineering 020302 automobile design & engineering 02 engineering and technology Acceleration Jerk 0203 mechanical engineering Automotive Engineering Artificial intelligence Limit (mathematics) Electrical and Electronic Engineering business Simulation |
| Zdroj: | IEEE Transactions on Vehicular Technology. 69:9315-9329 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2020.2980197 |
| Popis: | This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors. Deep learning techniques were used to extract latent features from the collected data. Extracted features were clustered into behaviors and used to create velocity profiles allowing an autonomous driving agent could drive in a human-like manner. By using proactive driving behaviors, the agent could limit potential sources of discomfort such as jerk and uncomfortable velocities. Additionally, we proposed a method to compare trajectories where not only the geometric similarity is considered, but also velocity, acceleration and jerk. Experimental results in a simulator implemented in ROS show that the autonomous agent built with the driving behaviors was capable of driving similarly to expert human drivers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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