Real-Time Path Rerouting and Obstacle Aware Navigation in Autonomous Vehicles: A Simulation and Data Analysis Approach

Abstract

Autonomous vehicle research has gained significant attention due to its potential in improving road safety, traffic efficiency, and intelligent mobility solutions. However, real-world testing remains costly and complex, making simulation-based models an effective approach for validating navigation and obstacle avoidance strategies. In this project, we present a simulation-driven autonomous vehicle framework capable of navigating between user-defined source and destination coordinates while ensuring real-time obstacle detection, dynamic rerouting, and journey visualization. The methodology integrates a virtual GPS for location tracking, an A*-based pathfinding algorithm enhanced with dynamic obstacle avoidance, and a simulation interface that allows users to input coordinates and visualize the entire navigation process. Camera-based or sensor-simulated modules are employed to detect obstacles in real time, triggering the rerouting logic to compute safe and collision-free alternative paths. Live data such as route progress, obstacle events, and estimated time of arrival are continuously displayed through the simulation dashboard. Following several iterations of testing, data logs were collected and analyzed using machine learning techniques to evaluate navigation efficiency, obstacle response time, and rerouting accuracy. Results demonstrate that the system successfully adapts to dynamic environments, offering a cost-effective and scalable platform for smart transportation research, algorithm benchmarking, and assistive mobility applications.