Real-time Trajectory Planning for Autonomous Vehicles in Dynamic Traffic Environments: A Survey of Modern Algorithms and Predictive Techniques

Abstract

The rapidly evolving field of autonomous vehicle (AV) technology necessitates an in-depth understanding of real-time trajectory planning methods to ensure safety, efficiency, and user comfort during navigation in intricate traffic conditions. This paper furnishes an exhaustive review of the prevalent algorithms and strategies employed in this domain. We commence with a detailed examination of Model Predictive Control (MPC), a potent optimization-based approach frequently adopted in contemporary applications. The paper then delves into the merits of sampling-based motion planning algorithms, emphasizing the innovations presented by Rapidly-exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*. Additionally, the Hybrid A* Algorithm, which amalgamates the principles of the A* grid-based search with differential constraints inherent to vehicles, is presented as a powerful tool for navigation, especially in spatially restrictive scenarios. A substantial section of the review is dedicated to deep learning techniques, underlining the predictive capacities of neural architectures such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. We also explore the elegant mathematical constructs of Bezier Curves and Splines, highlighting their utility in generating smooth, constraint-aware paths. The discussions further encompass methodologies like Potential Field Methods, Dynamic Window Approach (DWA), and state-of-the-art Reinforcement Learning techniques, with a spotlight on algorithms like Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO). A salient feature of advanced planning is the hierarchical paradigm, which combines coarse, high-level planning with refined, low-level trajectory optimization, proving invaluable in dynamic environments. Integral to these discussions is the need for predictive mechanisms, essential for anticipating and reacting to the behaviors of surrounding entities, be they pedestrians, cyclists, or other vehicles. In conclusion, this review not only offers a panoramic view of the current trajectory planning landscape but also extrapolates on the prospective advancements, hinting at a future where AI-driven planning exhibits unparalleled adaptability and proficiency in the most demanding traffic scenarios.