Survey of Autonomous Vehicles’ Collision Avoidance Algorithms
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 surve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/395 |
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| author | Meryem Hamidaoui Mohamed Zakariya Talhaoui Mingchu Li Mohamed Amine Midoun Samia Haouassi Djamel Eddine Mekkaoui Abdelkarim Smaili Amina Cherraf Fatima Zahra Benyoub |
| author_facet | Meryem Hamidaoui Mohamed Zakariya Talhaoui Mingchu Li Mohamed Amine Midoun Samia Haouassi Djamel Eddine Mekkaoui Abdelkarim Smaili Amina Cherraf Fatima Zahra Benyoub |
| author_sort | Meryem Hamidaoui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-01e90d9745354a91a7882213e713ab9e |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-01e90d9745354a91a7882213e713ab9e2025-01-24T13:48:46ZengMDPI AGSensors1424-82202025-01-0125239510.3390/s25020395Survey of Autonomous Vehicles’ Collision Avoidance AlgorithmsMeryem Hamidaoui0Mohamed Zakariya Talhaoui1Mingchu Li2Mohamed Amine Midoun3Samia Haouassi4Djamel Eddine Mekkaoui5Abdelkarim Smaili6Amina Cherraf7Fatima Zahra Benyoub8School of Software Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Software Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Software Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Mathematics, Abou-Bakr Belkaid University, Tlemcen 13000, AlgeriaSchool of Automation and Electrical Engineering, Beihang University, Beijing 100191, ChinaSince 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.https://www.mdpi.com/1424-8220/25/2/395collision avoidanceautonomous vehiclespath planningsensor-based approachesdecision-makingmachine learning |
| spellingShingle | Meryem Hamidaoui Mohamed Zakariya Talhaoui Mingchu Li Mohamed Amine Midoun Samia Haouassi Djamel Eddine Mekkaoui Abdelkarim Smaili Amina Cherraf Fatima Zahra Benyoub Survey of Autonomous Vehicles’ Collision Avoidance Algorithms Sensors collision avoidance autonomous vehicles path planning sensor-based approaches decision-making machine learning |
| title | Survey of Autonomous Vehicles’ Collision Avoidance Algorithms |
| title_full | Survey of Autonomous Vehicles’ Collision Avoidance Algorithms |
| title_fullStr | Survey of Autonomous Vehicles’ Collision Avoidance Algorithms |
| title_full_unstemmed | Survey of Autonomous Vehicles’ Collision Avoidance Algorithms |
| title_short | Survey of Autonomous Vehicles’ Collision Avoidance Algorithms |
| title_sort | survey of autonomous vehicles collision avoidance algorithms |
| topic | collision avoidance autonomous vehicles path planning sensor-based approaches decision-making machine learning |
| url | https://www.mdpi.com/1424-8220/25/2/395 |
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