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Efficient Perception, Planning, and Control Algorithms for Vision-Based Automated Vehicles
Autonomous vehicles have limited computational resources; hence, their control systems must be efficient.
End-to-End Multi-Task Deep Learning and Model Based Control Algorithm for Autonomous Driving
The DNN is a modified UNet, a well known encoder-decoder neural network of semantic segmentation.
End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving
Inspired by the UNet architecture of semantic image segmentation, we propose a lightweight UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane detection and path prediction (PP) in autonomous driving.
Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving
Based on the direct perception paradigm of autonomous driving, we investigate and modify the CNNs (convolutional neural networks) AlexNet and GoogLeNet that map an input image to few perception indicators (heading angle, distances to preceding cars, and distance to road centerline) for estimating driving affordances in highway traffic.
