Real-time sensor fusion and signal processing for enhanced collision avoidance in autonomous vehicles
*V. Dankan GowdaCorresponding authorpdf.Dankan@lincoln.edu.myFaculty of Built Science and EngineeringLincoln University CollegePetaling Jaya, Selangor Darul Ehsan, 47301, Malaysia0000-0003-0724-0333View full profile → , Shashi Kant Guptashashigupta@lincoln.edu.myFaculty of Built Science and EngineeringLincoln University CollegePetaling Jaya, Selangor Darul Ehsan 47301, Malaysia0000-0001-6587-5607View full profile → , K. D. V. Prasadkdv.prasad@sibmhyd.edu.inDepartment of ResearchSymbiosis Institute of Business ManagementHyderabad, Telangana, 509217, India0000-0001-9921-476XView full profile → , Parismita Sarmapari@gauhati.ac.inDepartment of Information TechnologyGauhati UniversityGuwahati, Assam, 781014, India0000-0003-1904-820XView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 31 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2236
- Pages:
- 2939–2953
Abstract
The purpose of this research is to design the real-time fusion and signal processing algorithms to enhance the collision avoidance in autonomous vehicles. The study integrates the acquired data as well as a variety of sources like radar, LIDAR and cameras in order to form a particular impression of the surrounding of the car. Some of the sensor fusion techniques that are embraced are the Advanced sensor fusion such as the Extended Kalman filtering (EKF) and the Unscented Kalman filtering (UKF), an aided fusion of the different sensors and improves the accuracy on detecting objects. The signal processing lines, noise suppression and feature enhancement are used to improve sensor data to allow more dependable decision-making in dynamic and unpredictable driving situations. The system incorporates machine learning models whether as deep learning algorithms to anticipate possible collision aspects and activate relevant safety interventions, including automated emergency braking and collision avoidance. The proposed system was proven to be effective through simulations in MATLAB that revealed great improvement in detection, increased response time, and stability in the system to be used in different real-world situations. The results underline the prospect of these methods making a considerable contribution to the vehicle safety in the case of autonomous driving implementation to the further increase in reliability and decrease in the risk of accidents in the situation of reality.
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References
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