Obstacle Detection and Avoidance in Self-Driving Carsusing LIDAR and Vision-Based Algorithms

Existing algorithms do not generalize well to the Light Detection and Ranging (LIDAR) and camera modality when trained to detect cars with a vision-based algorithm, or vice versa when LIDAR is used as a separate sensor. These systems would fail in severe conditions such as low lighting and inclement weather. Itpresents a system that combines Lidar and vision-based data utilizing sensor fusion to enable Lidar's depth perception capabilities and vision-based algorithms for encompassing object recognition tasks. Such technology increases obstacle detection, object classification, and real-time decision making to provide a safer autonomous navigation system. It combines Kalman Filtering, convolutional neural network (CNN) for object detection, density-based spatial clustering of applications with noise (DBSCAN) for LIDAR data clustering, support vector machine (SVM) for classification, and predictive path planning. The results demonstrate the following improvements: 96.5% detection accuracy, 1.5% false positive rate, and 0.2% collision rate. It is more robust and outperforms existing systems in real-world situations such as bad illumination, severe weather, and complex traffic scenarios.