Autonomus Vechicle Simulation System for Intelligent Transporation

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.