%0 Conference Paper %1 a26f0a0849ef4a32a6358c0b709982d3 %A Li, Dianzhao %A Okhrin, Ostap %B 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023 %C United States of America %D 2023 %I Institute of Electrical and Electronics Engineers Inc. %K topic_engineering FIS_scads imported %P 866--873 %R 10.1109/ITSC57777.2023.10422677 %T Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer %U https://2023.ieee-itsc.org/ %X To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamental and well-studied driving ones. However, previous studies have mainly focused on individual tasks, and car following tasks have typically relied on complete leader-follower information to attain optimal performance. To address this limitation, we propose a vision-based deep reinforcement learning (DRL) agent that can simultaneously perform lane keeping and car following maneuvers. To evaluate the performance of our DRL agent, we compare it with a baseline controller and use various performance metrics for quantitative analysis. Furthermore, we conduct a real-world evaluation to demonstrate the Sim2Real transfer capability of the trained DRL agent. To the best of our knowledge, our vision-based car following and lane keeping agent with Sim2Real transfer capability is the first of its kind. We have made the codes and the videos of the simulation and real-world evaluation accessible online11Code and videos are available on: https://github.com/DailyL/Sim2Realautonomous_vehicle.

@inproceedings{a26f0a0849ef4a32a6358c0b709982d3, abstract = {To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamental and well-studied driving ones. However, previous studies have mainly focused on individual tasks, and car following tasks have typically relied on complete leader-follower information to attain optimal performance. To address this limitation, we propose a vision-based deep reinforcement learning (DRL) agent that can simultaneously perform lane keeping and car following maneuvers. To evaluate the performance of our DRL agent, we compare it with a baseline controller and use various performance metrics for quantitative analysis. Furthermore, we conduct a real-world evaluation to demonstrate the Sim2Real transfer capability of the trained DRL agent. To the best of our knowledge, our vision-based car following and lane keeping agent with Sim2Real transfer capability is the first of its kind. We have made the codes and the videos of the simulation and real-world evaluation accessible online11Code and videos are available on: https://github.com/DailyL/Sim2Realautonomous_vehicle.}, added-at = {2024-11-28T16:27:18.000+0100}, address = {United States of America}, author = {Li, Dianzhao and Okhrin, Ostap}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2c74f020d7da8646c0a792bde9f998090/scadsfct}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023}, doi = {10.1109/ITSC57777.2023.10422677}, interhash = {1bddce5794f09c3ddf772d6b2d334f2f}, intrahash = {c74f020d7da8646c0a792bde9f998090}, keywords = {topic_engineering FIS_scads imported}, language = {English}, note = {Publisher Copyright: {\textcopyright} 2023 IEEE.; 26th IEEE International Conference on Intelligent Transportation Systems : Towards a New Era of Human-aware, Human-interactive, and Human-friendly ITS, ITSC 2023 ; Conference date: 24-09-2023 Through 28-09-2023}, pages = {866--873}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, series = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC}, timestamp = {2024-11-28T17:41:00.000+0100}, title = {Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer}, url = {https://2023.ieee-itsc.org/}, year = 2023 }