Hamidaoui Meryem, Talhaoui Mohamed Zakariya, Li Mingchu, Midoun Mohamed Amine, Haouassi Samia, Mekkaoui Djamel Eddine, Smaili Abdelkarim, Cherraf Amina, Benyoub Fatima Zahra
School of Software Technology, Dalian University of Technology, Dalian 116024, China.
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2025 Jan 10;25(2):395. doi: 10.3390/s25020395.
Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations. The survey also emphasizes how Machine Learning methods can improve the efficacy of obstacle avoidance. Combined, these techniques are necessary for enhancing the dependability and safety of autonomous driving systems, ultimately increasing public confidence in this game-changing technology.
由于自动驾驶汽车领域发展迅速，对它们来说，安全有效地在周围环境中导航以避免碰撞变得越来越关键。这项全面的调查研究了目前自动驾驶汽车所采用的主要防撞算法。它探讨了多种方法，比如用于精确障碍物识别的基于传感器的方法、确保汽车遵循可靠且安全路径的复杂路径规划算法，以及能够对一系列驾驶情况做出适应性反应的决策系统。该调查还强调了机器学习方法如何能够提高避障的效率。综合起来，这些技术对于提高自动驾驶系统的可靠性和安全性是必不可少的，最终会增强公众对这项变革性技术的信心。
