Research on Parallel Testing Methods for Autonomous Driving Based on CARLA

Engineering notes

Key topics: autonomous driving system, autonomous driving, self-driving car, self-driving, carla. See the paper for implementation details and experimental results.

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

The rapid development of computer vision and artificial intelligence technology has made overcoming the limitations of high cost and scene coverage in the physical testing of advanced self-driving cars a key challenge for the industry. Current physical testing methods are based on real vehicle road tests, which present two key challenges: economic issues and inadequate scenario coverage. Meanwhile, existing research tends to focus on validating algorithms in fixed scenarios. In response to the limitat mentioned, this paper proposes a parallel scenario testing method that combines the CARLA simulation platform with the YOLOv8-based detection algorithm. This method constructs a multidimensional testing framework that incorporates natural factors and traffic complexity indexes. It also verifies that the CARLA-YOLOv8 parallel testing framework can effectively quantify the robustness of the algorithm under pressure in a multidimensional environment. The framework introduces a new approach to the virtual verification of autonomous driving systems. This is particularly important for addressing safety-critical edge cases and scalability issues.

Links and sources

• Official / arXiv page

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