3 papers
cs.RO2026
Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
Qitao Weng, Heechul Yun
Latency-accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving, in particular, safety depends…
cs.RO2024
TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing
Mohammed Misbah Zarrar, Qitao Weng, Bakhbyergyen Yerjan +2
Prior research has demonstrated the effectiveness of end-to-end deep learning for robotic navigation, where the control signals are directly derived from raw sensory data. However,…
cs.LG2022
DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems
Michael Bechtel, QiTao Weng, Heechul Yun
Running deep neural networks (DNNs) on tiny Micro-controller Units (MCUs) is challenging due to their limitations in computing, memory, and storage capacity. Fortunately, recent ad…