Survey of Autonomous Vehicles' Collision Avoidance Algorithms

Overview

Journal Sensors (Basel)

Publisher MDPI

Date 2025 Jan 25

PMID 39860765

Authors

Meryem Hamidaoui

Mohamed Zakariya Talhaoui

Mingchu Li

Mohamed Amine Midoun

Samia Haouassi

Djamel Eddine Mekkaoui

Abdelkarim Smaili

Amina Cherraf

Fatima Zahra Benyoub

Affiliations

Soon will be listed here.

Abstract

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.

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