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Found 6 papers, 1 papers with code
Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning
Based on our experiments and comprehensive analysis of the proposed method, the results demonstrate that our proposed method enables AGV to more rapidly complete path planning tasks with continuous actions in our environments.
Multi-objective path tracking control for car-like vehicles with differentially bounded n-smooth output
The main contribution of this work is a control law designed to produce a smooth steering angle with implicit satisfaction of bounds on its derivatives.
An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars
The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP.
Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors
We develop both a hardware setup consisting of a camera and a traffic surveillance radar and a trajectory extraction algorithm.
Pedestrian Collision Avoidance System for Scenarios with Occlusions
We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.
Safe learning-based optimal motion planning for automated driving
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic.
