Autonomous Vehicle Driving in Harsh Weather: Adaptive Fusion Alignment Modeling and Analysis.

The achievements of high driving performance and error minimization of autonomous vehicles (AVs) in harsh weather are the biggest challenges for the society of autonomous research area. AVs are mainly driven by the sensor fusion technology of light detection and ranging (LiDAR), radio detection and ranging (RADAR), and camera sensors. In harsh weather such as rain, storm, law lighting, snowfall, and vapor, the detection performances of all the sensors are obstructed. The camera imaging for object detection systems is highly affected by different types of noise in adverse weather conditions and its performance is very anxious for error-free AV driving. This article proposes the prediction-based adaptive fusion alignment (AFA) algorithm of the robust path and object tracking systems with the deep convolutional neural networking (D-CNN) model for detection accuracy improvement, calculative error reduction, and overall driving error minimization of AVs in harsh weather conditions. RADAR and LiDAR are not deep learning (DL) based yet. The D-CNN model of DL algorithms for camera image processing and the segmentation process of object classification is used for actual object detection and localization. The AV-simulated driving accuracy in harsh weather is significantly increased with the proposed AFA and D-CNN algorithms. [ABSTRACT FROM AUTHOR]