Enhancing Autonomous Vehicle Navigation in Complex Environment With Semantic Proto-Reinforcement Learning

Despite great progress in autonomous vehicle (AV) navigation, the technical challenges within this space are still considerable when it comes to successful integration of AVs into complex real-world environments. To tackle these challenges, this paper presents a new semantic proto-reinforcement learning (SP-RL) method for dynamic path planning and real-time obstacle avoidance of an autonomous car that can be adapted to various weather conditions in localization-deficient environments, while predicting the human intentions of different shapes on road. This approach seeks to improve navigation capability of AV in dynamic and unstructured environments, as well as to address real-time detection and avoidance response for obstacles more promptly while being able to adapt its decision-making system based on weather condition by using the semantic graph network (SGN) within segmentation process therefore enhanced version configured with prototype-based reinforcement learning (PRL). This innovation is new competitive edge compared previous existing approaches. Dynamic SGN is used to segment challenging 3D and free space environments, so that the AV can comprehend highly unintuitive areas like parking lots, construction site conditionals, or off-road scenarios. At the same time, PRL is used to help real-time decision-making so the AV can quickly and precisely respond unexpected obstacles or changing environments. This approach is confirmed to be effective by extensive testing in the CARLA simulation environment, showing substantial improvement of AV navigation capability. This work demonstrates an exciting step towards solving the most fundamental problems faced by autonomous vehicles and could help to ensure that future AV systems are safer, more robust, and adaptable than current ones. It is appliable for urban areas which represent high volumes of pedestrians and vehicles, industrial sites with changing conditions that may be unpredictable at times or the challenging off-road areas in rural geographies where typical terrains are rough, uneven, rugged yet not well-structured. The model is robust to diverse weather conditions that make AV operations more reliable and safer. The model was tested on root mean square error (RMSE), computational time, no crash, obstacles avoidance, and success rate obtaining overall 98%.