Human-Like Autonomous Driving on Dense Traffic
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911790664777728 |
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| author | Yildirim, Mustafa Fallah, Saber |
| author_facet | Yildirim, Mustafa Fallah, Saber |
| contents | This paper proposes a imitation learning model for autonomous driving on highway traffic by mimicking human drivers' driving behaviours. The study utilizes the HighD traffic dataset, which is complex, high-dimensional, and diverse in vehicle variations. Imitation learning is an alternative solution to autonomous highway driving that reduces the sample complexity of learning a challenging task compared to reinforcement learning. However, imitation learning has limitations such as vulnerability to compounding errors in unseen states, poor generalization, and inability to predict outlier driver profiles. To address these issues, the paper proposes mixture density network behaviour cloning model to manage complex and non-linear relationships between inputs and outputs and make more informed decisions about the vehicle's actions. Additional improvement is using collision penalty based on the GAIL model. The paper includes a simulated driving test to demonstrate the effectiveness of the proposed method based on real traffic scenarios and provides conclusions on its potential impact on autonomous driving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02477 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Human-Like Autonomous Driving on Dense Traffic Yildirim, Mustafa Fallah, Saber Robotics This paper proposes a imitation learning model for autonomous driving on highway traffic by mimicking human drivers' driving behaviours. The study utilizes the HighD traffic dataset, which is complex, high-dimensional, and diverse in vehicle variations. Imitation learning is an alternative solution to autonomous highway driving that reduces the sample complexity of learning a challenging task compared to reinforcement learning. However, imitation learning has limitations such as vulnerability to compounding errors in unseen states, poor generalization, and inability to predict outlier driver profiles. To address these issues, the paper proposes mixture density network behaviour cloning model to manage complex and non-linear relationships between inputs and outputs and make more informed decisions about the vehicle's actions. Additional improvement is using collision penalty based on the GAIL model. The paper includes a simulated driving test to demonstrate the effectiveness of the proposed method based on real traffic scenarios and provides conclusions on its potential impact on autonomous driving. |
| title | Human-Like Autonomous Driving on Dense Traffic |
| topic | Robotics |
| url | https://arxiv.org/abs/2310.02477 |