Cross-Scenario End-to-End Motion Planning in Off-Road Environment: A Lifelong Learning Perspective

Motion planning in off-road scenarios is particularly challenging due to diverse terrain features, surface characteristics, and environmental factors. Consequently, rule-based or fixed-parameter motion planning methods often fail to maintain optimal performance, especially in cross-scenario applications. To address these issues, we propose an innovative method for end-to-end motion planning in off-road cross-scenario applications that leverages lifelong learning. We employ a multi-layer map to represent various terrain features and a Transformer network to emulate human motion planning in diverse off-road environments. Additionally, we constructed a structured scene memory library to support our lifelong learning algorithm, enabling effective knowledge retention and transfer across different scenarios. This ensures robust performance even in data-scarce environments. Experimental results demonstrate that our method significantly improves performance in data-scarce off-road scenarios while ensuring robust adaptability and scalability across diverse and new scenarios.