Autonomus Vechicle Simulation System for Intelligent Transporation

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lane detection, semantic segmentation, object detection, lidar, carla, tesla, radar, perception, prediction. 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

Autonomous vehicles are rapidly reshaping intelligent transportation systems by reducing human intervention, enhancing safety, and improving traffic efficiency. This paper presents the Tesla Autonomous Emergency AI Dashboard: a high-fidelity, single-file autonomous vehicle simulation framework built on CARLA 0.9.11 and rendered in real-time via pygame. The system integrates a multi-modal perception layer comprising a virtual 32-channel LiDAR, RGB front and rear cameras, a semantic segmentation camera, and a forward-facing radar sensor. Deep learning inference using YOLOv8n performs real-time detection of vehicles, pedestrians, and emergency scenarios at 20Hz. Seven purpose-built feature modules—mini-map tracking, data logging, dynamic weather, speed HUD, TTC-based collision prediction, OpenCV lane detection with lane-keep assist, and LiDAR/radar visualisation—are fully merged into a single executable. The dashboard faithfully replicates a professional automotive HUD with a 1400×830 pygame window, displaying four camera tiles, an Explainable AI panel, a sensor status panel, an analog speedometer, throttle/brake/steer bars, a compass, and a five-section status bar with contextual icons. Evaluation across eight weather presets demonstrates 92% daytime and 87% night-time object detection accuracy, sub-200ms response times, 98% collision avoidance success, and 100% emergency vehicle compliance.

Links and sources

• PDF from original source

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