Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …
Z Feng, S Guo, X Tan, K Xu… - Proceedings of the …, 2022 - openaccess.thecvf.com
This paper presents a novel parametric curve-based method for lane detection in RGB images. Unlike state-of-the-art segmentation-based and point detection-based methods that …
Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human …
The recent proliferation of computing technologies (eg, sensors, computer vision, machine learning, and hardware acceleration) and the broad deployment of communication …
Z Qin, H Wang, X Li - Computer Vision–ECCV 2020: 16th European …, 2020 - Springer
Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed. Inspired by …
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system …
JE Office, J Chen, H Dan, Y Ding, Y Gao, M Guo… - Journal of Traffic and …, 2021 - Elsevier
Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly …
T Zheng, H Fang, Y Zhang, W Tang, Z Yang… - Proceedings of the …, 2021 - ojs.aaai.org
Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (eg, severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals …
Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw …