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Found 5 papers, 0 papers with code
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.
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.
Multi-task UNet architecture for end-to-end autonomous driving
We propose an end-to-end driving model that integrates a multi-task UNet (MTUNet) architecture and control algorithms in a pipeline of data flow from a front camera through this model to driving decisions.
Efficient Perception, Planning, and Control Algorithms for Vision-Based Automated Vehicles
Autonomous vehicles have limited computational resources; hence, their control systems must be efficient.
Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR
The accurate prediction of smooth steering inputs is crucial for autonomous vehicle applications because control actions with jitter might cause the vehicle system to become unstable.
