Training traffic light behavior with end-to-end learning
| Authors | |
|---|---|
| Publication date | 2023 |
| Host editors |
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| Book title | Intelligent Autonomous Systems 17 |
| Book subtitle | Proceedings of the 17th International Conference IAS-17 |
| ISBN |
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| ISBN (electronic) |
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| Series | Lecture Notes in Networks and Systems |
| Event | 17th International Conference on Intelligent Autonomous Systems |
| Pages (from-to) | 753-764 |
| Number of pages | 12 |
| Publisher | Cham: Springer |
| Organisations |
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| Abstract |
In this work, we study neural network architectures that will reduce the number of infractions made by autonomous-driving agents. These agents control vehicles by providing future waypoints directly from a forward-facing camera. Building on top of the teacher-student approach of Cheating by Segmentation, we investigate the impact of Pyramid Pooling Module and Feature Pyramid Network with the aim to learn more representative features. We run our experiment with CARLA simulator
and show that pyramid perception modules have a positive impact in reducing the number of traffic light infractions and collisions. |
| Document type | Conference contribution |
| Language | English |
| Published at | https://doi.org/10.1007/978-3-031-22216-0_50 |
| Other links | https://puh.srce.hr/s/sQKWxRwLdK5BCoW |
| Downloads |
traffic_light_behavior_trained_with_end_to_end_learning
(Accepted author manuscript)
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| Permalink to this page | |
