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Found 6 papers, 0 papers with code
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning
In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems.
Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection
Moreover, previous ML-based approaches predominantly utilize basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce Surrogate Safety Measures (SSMs) as the input features for ML models to improve the detection performance.
Mode substitution induced by electric mobility hubs: results from Amsterdam
To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction.
Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning
Experimental results on various testing scenarios reveal that the TRPO algorithm outperforms DDPG and PPO in terms of safety and efficiency, and PPO performs best in terms of comfort level.
Framework for Network-Constrained Tracking of Cyclists and Pedestrians
This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e. g., pedestrians or cyclists) constrained by a road network, using mobile (e. g., vehicles) spatially distributed sensor platforms.
A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection
Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated.
