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Wenjie Song
Researcher at Beijing Institute of Technology
Publications - 32
Citations - 387
Wenjie Song is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Trajectory. The author has an hindex of 6, co-authored 21 publications receiving 187 citations. Previous affiliations of Wenjie Song include Princeton University.
Papers
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Journal ArticleDOI
Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision
TL;DR: A lightweight stereo vision-based driving lane detection and classification system to achieve the ego-car’s lateral positioning and forward collision warning to aid advanced driver assistance systems (ADAS).
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Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System
TL;DR: The proposed method makes full use of the unique advantages of stereo cameras and mmw-radar to sense the environment through several modules and proves that it can work effectively even though the ego-vehicle drives quickly.
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Multi-camera visual SLAM for off-road navigation
TL;DR: This work proposes a panoramic vision SLAM method based on multi-camera collaboration, aiming at utilizing the characters of pan oramic vision and stereo perception to improve the localization precision in off-road environments.
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Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model
TL;DR: Wang et al. as discussed by the authors proposed an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories, which is used to approximate the distribution of the predicted trajectory and also serves as a reference for credibility evaluation.
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Stabilization Approaches for Reinforcement Learning-Based End-to-End Autonomous Driving
TL;DR: Two approaches to improve the stability of the policy model training with as few manual data as possible are proposed, which can converge quickly and stably in Gazebo in which previous methods can hardly converge.