End-to-end autonomous driving using the Ape-X algorithm in Carla simulation environment

Engineering notes

By examining the state-of-the-art works in the domain of DRL for autonomous driving and inspired from the work of [1], we have designed an end-to-end autonomous driving system using the Ape-X algorithm [2] in Carla simulation environment [3] and have evaluated the performance by comparing its results to those that are obtained using other DRL techniques.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Despite great advances in controlling vehicles for autonomous driving and in deep reinforcement learning (DRL) techniques, designing an end-to-end architecture that supports autonomous driving using DRL techniques while facing uncertainties in complex and dynamic environments still remains challenging. By examining the state-of-the-art works in the domain of DRL for autonomous driving and inspired from the work of [1], we have designed an end-to-end autonomous driving system using the Ape-X algorithm [2] in Carla simulation environment [3] and have evaluated the performance by comparing its results to those that are obtained using other DRL techniques.

Links and sources

• Official / arXiv page

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