Enhancing Obstacle Tracing in Self-Driving Cars by Combining Infrared Camera and Radar Sensor.

Building safe and reliable autonomous vehicles depend on accurate and efficient obstacle detection and tracing systems. On the other hand, this paper focuses on improving obstacle tracing in autonomous cars by employing infrared sensors and radar cameras within the framework of Bayesian networks. The proposed system merges the capabilities of the two sensors to enhance the fidelity and consistency of the interpretation of the external environment concerning obstacle detection. A Bayesian network is used to describe the uncertainty associated with each sensor, obstacle complexity, and environmental variables which, in turn, enable real-time interaction and dynamic control. The experiments' results are a good validation of the hypothesis, showing that the proposed system enhances the accuracy of obstacle tracing, decreases the number of false positives and negatives, and improves the safety and efficacy of self-driving cars. Keywords - Self-driving cars, Bayesian Networks, Infrared Sensors, Radar Cameras, Obstacle Detection, Sensor fusion.