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1. Traffic4D: Single View Longitudinal 4D Reconstruction of Repetitious Activity using Self-Supervised Experts

   Li, Fangyu; Reddy, N. Dinesh; Chen, Xudong; Narasimhan, Srinivasa G. (July 2021, IEEE Intelligent Vehicles Symposium)

   Reconstructing 4D vehicular activity (3D space and time) from cameras is useful for autonomous vehicles, commuters and local authorities to plan for smarter and safer cities. Traffic is inherently repetitious over long periods, yet current deep learning-based 3D reconstruction methods have not considered such repetitions and have difficulty generalizing to new intersection-installed cameras. We present a novel approach exploiting longitudinal (long-term) repetitious motion as self-supervision to reconstruct 3D vehicular activity from a video captured by a single fixed camera. Starting from off-the-shelf 2D keypoint detections, our algorithm optimizes 3D vehicle shapes and poses, and then clusters their trajectories in 3D space. The 2D keypoints and trajectory clusters accumulated over long-term are later used to improve the 2D and 3D keypoints via self-supervision without any human annotation. Our method improves reconstruction accuracy over state of the art on scenes with a significant visual difference from the keypoint detector’s training data, and has many applications including velocity estimation, anomaly detection and vehicle counting. We demonstrate results on traffic videos captured at multiple city intersections, collected using our smartphones, YouTube, and other public datasets. 

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2. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   https://doi.org/10.1109/MT-ITS49943.2021.9529289  

   Jiang, Liming; Xie, Yuanchang; Wen, Xiao; Chen, Danjue; Li, Tienan; Evans, Nicholas G. (January 2021, 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using real-world vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically. 

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3. Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR

   https://doi.org/10.1109/TITS.2023.3268281  

   Bai, Zhengwei; Nayak, Saswat P; Zhao, Xuanpeng; Wu, Guoyuan; Barth, Matthew J; Qi, Xuewei; Liu, Yongkang; Sisbot, Emrah Akin; Oguchi, Kentaro (September 2023, IEEE Transactions on Intelligent Transportation Systems)

   Abstract—Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of 0.69m and 0.33m for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of 3 − 4Hz. 

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4. City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

   https://doi.org/10.1145/3341162.3349336  

   Wei, Peter; Shi, Haocong; Yang, Jiaying; Qian, Jingyi; Ji, Yinan; Jiang, Xiaofan (September 2019, Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers)

   Vehicle flow estimation has many potential smart cities and transportation applications. Many cities have existing camera networks which broadcast image feeds; however, the resolution and frame-rate are too low for existing computer vision algorithms to accurately estimate flow. In this work, we present a computer vision and deep learning framework for vehicle tracking. We demonstrate a novel tracking pipeline which enables accurate flow estimates in a range of environments under low resolution and frame-rate constraints. We demonstrate that our system is able to track vehicles in New York City's traffic camera video feeds at 1 Hz or lower frame-rate, and produces higher traffic flow accuracy than popular open source tracking frameworks. 

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5. Partial trajectory method to align and validate successive video cameras for vehicle tracking

   https://doi.org/10.1016/j.trc.2023.104416  

   Coifman, Benjamin; Li, Lizhe (January 2024, Transportation Research Part C: Emerging Technologies)

   This paper develops the partial trajectory method to align the views from successive fixed cameras that are used for video-based vehicle tracking across multiple camera views. The method is envisioned to serve as a validation tool of whatever alignment has already been performed between the cameras to ensure high fidelity with the actual vehicle movements as they cross the boundaries between cameras. The strength of the method is that it operates on the output of vehicle tracking in each camera rather than secondary features visible in the camera view that are unrelated to the traffic dynamics (e.g., fixed fiducial points). Thereby providing a direct feedback path from the tracking to ensure the quality of the alignment in the context of the traffic dynamics. The method uses vehicle trajectories within successive camera views along a freeway to deduce the presence of an overlap or a gap between those cameras and quantify how large the overlap or gap is. The partial trajectory method can also detect scale factor errors between successive cameras. If any error is detected, ideally one would redo the original camera alignment, if that is not possible, one could use the calculations from the algorithm to post hoc address the existing alignment. This research manually re-extracted the individual vehicle trajectories within each of the seven camera views from the NGSIM I-80 dataset. These trajectories are simply an input to the algorithm. The resulting method transcends the dataset and should be applicable to most methods that seek to extract vehicle trajectories across successive cameras. That said, the results reveal fundamental errors in the NGSIM dataset, including unaccounted for overlap at the boundaries between successive cameras, which leads to systematic speed and acceleration errors at the six camera interfaces. This method also found scale factor errors in the original NGSIM homographies. In response to these findings, we identified a new aerial photo of the NGSIM site and generated new homographies. To evaluate the impact of the partial trajectory method on the actual trajectory data, the manually re-extracted data were projected into the new coordinate system and smoothed. The re-extracted data shows much greater fidelity to the actual vehicle motion. The re-extracted data also tracks the vehicles over a 14% longer distance and adds 23% more vehicles compared to the original NGSIM dataset. As of publication, the re-extracted data from this paper will be released to the research community. 

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