Robust Control Strategies for Autonomous Vehicles in Varied Traffic Conditions

Abstract

The advancement of autonomous vehicles (AVs) is largely reliant on robust control strategies capable of managing diverse and unpredictable traffic situations. This study discusses six key strategies commonly used in the design and operation of AVs: Model Predictive Control (MPC), Reinforcement Learning (RL), Fuzzy Logic, Sliding Mode Control (SMC), Genetic Algorithms, and Neural Networks. MPC offers a method of predicting and optimizing system behavior over time, a valuable tool for handling changing traffic conditions. RL provides a mechanism for learning from the environment and adjusting vehicle behavior accordingly, using a reward system. Fuzzy Logic, with its basis in human-like reasoning, adapts well to unpredictable traffic situations. SMC is advantageous in its robustness to uncertainties and nonlinearities in traffic conditions. Genetic Algorithms offer an approach for evolving resilient control strategies by simulating traffic conditions and selecting the highest-performing strategies. Finally, Neural Networks, especially Convolutional Neural Networks (CNNs), process sensor data for perception tasks such as object detection and depth estimation. However, the effectiveness of these control strategies is dependent on accurate environmental perception and prediction, which remain significant research challenges. This involves the use of advanced sensing technology and sophisticated algorithms to interpret the sensor data accurately. Ultimately, the study emphasizes the need for these control strategies to account for a broad spectrum of potential traffic scenarios, underscoring the complexity and breadth of ongoing research in this field.