# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 2 Related Work
### Traditional Path-Planning Methods  
Numerous studies on path planning have focused on optimizing classical path-planning algorithms to address increasingly complex environments and specific application requirements. For autonomous-vehicle path planning, [PERSON] et al. (2019) proposed an improved A* algorithm that incorporates redundant safety spaces and predictive planning, generating shorter and smoother paths to enhance collision avoidance capabilities. In terms of hybrid methods, [PERSON] et al.'s study combined the enhanced A* algorithm with the DWA, utilizing bidirectional search, adaptive heuristics, and key node filtering to shorten path lengths and improve algorithm reliability ([PERSON] et al., 2019). [PERSON] et al. (2019) improved the Lifelong Planning A* (LPA*) algorithm by introducing a path priority partitioning mechanism, dividing paths into high- and low-priority categories to maintain the stability of high-priority paths. This method effectively avoids path conflicts by weighting high-priority path nodes, thereby enhancing the efficiency and stability of multi-path planning.  
[PERSON] et al., in a study on autonomous driving ([PERSON] et al., 2019), integrated environmental and vehicle constraints to propose an improved RRT-Connect algorithm. By designing appropriate path cost functions and heuristic guidance strategies, the method significantly reduced path lengths and planning times, expanding the path-planning capabilities of intelligent vehicles in complex environments. For mobile vehicles in complex unknown environments, [PERSON] et al. (2019) optimized the RRT-Connect algorithm through goal-biased strategies and node selection, improving path smoothness and success rates.  
In dynamic environments, [PERSON] et al. (2019) combined Artificial Potential Fields (APFs) with RRT methods, rapidly generating collision-free local paths through weighted obstacle configurations, yielding improved replanning efficiency. In other developments, the virtual potential field combined with RRT (VPF-RRT) algorithm ([PERSON] et al., 2019) and the convex decomposition topology-based RRT (CDT-RRT) algorithm ([PERSON] et al., 2019) were designed to optimize the path smoothness and the performance in exploring the shortest paths, respectively. For robotic-arm path planning, [PERSON] et al. (2019) proposed the PS-RRT algorithm based on partitioned dynamic sampling and node rejection mechanisms, effectively reducing the number of invalid nodes and achieving efficient path planning.  
Traditional path-planning methods largely rely on predefined environmental maps, making it difficult to cope with highly dynamic or unknown environmental changes, limiting their real-time application capabilities. Many optimization algorithms exhibit high computational complexity in complex environments, affecting the real-time performance and efficiency of path planning. These methods typically focus on the geometric optimization of paths, such as optimizing path length and smoothness, while neglecting the causal relationships and environmental interference factors in the path-planning process, resulting in insufficient robustness in dynamic environments.