A Lane Recognition Based on Line-CNN Network

Qiao, Dan; Wang, Xiaoyu; Wang, Tiantian

doi:10.1109/ipec49694.2020.9114966

Cited by 8 publications

(2 citation statements)

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“…This method optimizes the spatial embedding of pixels directly by adopting pixel coordinates. Line‐CNN (Qiao et al., 2020) can predict any number of lanes without postprocessing operations. However, it has poor detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

A real‐time lane detection network using two‐directional separation attention

Zhang

Jiang

Yang

et al. 2023

Computer aided Civil Eng

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Real‐time network by adopting attention mechanism is helpful for enhancing lane detection capability of autonomous vehicles. This paper proposes a real‐time lane detection network (TSA‐LNet) that incorporates a lightweight network (LNet) and a two‐directional separation attention (TSA) to enhance the lane detection capability of autonomous vehicles. By adopting the attention mechanism, the real‐time performance and detection accuracy are significantly improved. Specifically, LNet employs symmetry layer to drastically reduce the number of parameters and the network's running time. TSA infers the attention map along two separate directions, transverse and longitudinal, and performs adaptive feature refinement by multiplying the attention map with the input feature map. TSA can be integrated into LNet to capture the local textural and global contextual information of lanes without increasing the processing time. Results on popular benchmarks demonstrate that TSA‐LNet achieves outstanding detection accuracy and faster speed (6.99 ms per image). Additionally, TSA‐LNet exhibits excellent robustness in real‐world scenarios.

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Section: Related Workmentioning

confidence: 99%

A real‐time lane detection network using two‐directional separation attention

Zhang

Jiang

Yang

et al. 2023

Computer aided Civil Eng

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“…Similarly, in [119], mask region CNN (mask R-CNN) is used for an LDW system. Some other works, e.g., [120], [121], [122], [123], [124], and [125], exploit CNN for the lane detection function. The researchers in [126] used a K-NN and SVM-based lane detection system.…”

Section: E State-of-the-art Machine Learning Algorithms For Adas Appl...mentioning

confidence: 99%

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar

Hong

Moore

2023

IEEE Open J. Veh. Technol.

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Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

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