323 citations · 350 across the 11 of their papers we have counts for
11 papers
DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles
Sebastian Huch, Florian Sauerbeck, Johannes Betz
Autonomous vehicles demand high accuracy and robustness of perception algorithms. To develop efficient and scalable perception algorithms, the maximum information should be extract…
Drive Right: Promoting Autonomous Vehicle Education Through an Integrated Simulation Platform
Zhijie Qiao, Helen Loeb, Venkata Gurrla +3
Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these…
Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks
Zirui Zang, Hongrui Zheng, Johannes Betz +1
Robot localization is an inverse problem of finding a robot's pose using a map and sensor measurements. In recent years, Invertible Neural Networks (INNs) have successfully solved…
Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom
Johannes Betz, Hongrui Zheng, Zirui Zang +8
Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in devel…
A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
Xiatao Sun, Mingyan Zhou, Zhijun Zhuang +3
Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driv…
Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor
Benjamin David Evans, Johannes Betz, Hongrui Zheng +3
Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the rob…