29,010 research outputs found
A revised video vision transformer for traffic estimation with fleet trajectories
Real-time traffic monitoring represents a key component for transportation management. The increasing penetration rate of connected vehicles with positioning devices encourages the utilization of trajectory data for real-time traffic monitoring. The use of commercial fleet trajectory data could be seen as the first step towards mobile sensing networks. The main objective of this research is to estimate space occupancy of a single road segment with partially observed trajectories (commercial fleet trajectories in our case). We first formulate the trajectory-based traffic estimation as a video computing problem. Then, we reconstruct trajectory series into video-like data by performing spatial discretization. Following this, video input is embedded using a tubelet embedding strategy. Finally, a Revised Video Vision Transformer (RViViT) is proposed to estimate traffic state from video embeddings. The proposed RViViT is tested on a public dataset of naturalistic vehicle trajectories collected from German highways around Cologne during 2017 and 2018. The results witness the effectiveness of the proposed method in traffic estimation with partially observed trajectories
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
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