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The table below highlights performance metrics under different observability settings, including full observability, field-of-view (FOV), and recursive field-of-view (SFOV). These settings allow agents to operate with varying degrees of environmental awareness, impacting their ability to plan and execute maneuvers effectively. - | Observability Performance Metrics | | :-------------------------------: | | ![Observability Performance](https://ucd-dare.github.io/cardreamer.github.io/static/images/tables/observability_performance.png) | @@ -263,6 +261,7 @@ We stream observations, rewards, terminal conditions, and custom metrics to a we ## :hammer: System + ... To easily customize your own driving tasks, and observation spaces, etc., please refer to our [CarDreamer API Documents](https://car-dreamer.readthedocs.io/en/latest/). @@ -279,8 +278,8 @@ If you find this repository useful, please cite this paper: ``` @ARTICLE{10714437, author={Gao, Dechen and Cai, Shuangyu and Zhou, Hanchu and Wang, Hang and Soltani, Iman and Zhang, Junshan}, - journal={IEEE Internet of Things Journal}, - title={CarDreamer: Open-Source Learning Platform for World Model Based Autonomous Driving}, + journal={IEEE Internet of Things Journal}, + title={CarDreamer: Open-Source Learning Platform for World Model Based Autonomous Driving}, year={2024}, volume={}, number={}, @@ -319,6 +318,7 @@ If you find this repository useful, please cite this paper: Special thanks to the community for your valuable contributions and support in making CarDreamer better for everyone! +