aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge

Burnett, Keenan; Samavi, Sepehr; Waslander, Steven L.; Barfoot, Timothy D.; Schoellig, Angela P.

doi:10.1109/crv.2019.00036

Cited by 24 publications

(6 citation statements)

References 22 publications

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“…Figure 11 illustrates the aUToTrack pipeline. In Burnett, Samavi, Waslander, Barfoot, and Schoellig (2019), we demonstrated that aUToTrack accurately estimates the position and velocity of pedestrians on both the KITTI Object Tracking benchmark and our own dataset, UofTPed50. We have made this dataset publicly available, and it can be accessed using the link below.…”

Section: Object Detection and Trackingmentioning

confidence: 72%

Zeus: A system description of the two‐time winner of the collegiate SAE autodrive competition

Burnett

Qian

et al. 2020

Journal of Field Robotics

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The SAE AutoDrive Challenge is a 3‐year collegiate competition to develop a self‐driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock town built for self‐driving car testing at the University of Michigan. Teams were required to autonomously navigate a series of intersections while handling pedestrians, traffic lights, and traffic signs. Zeus is aUToronto's winning entry in the AutoDrive Challenge. This article describes the system design and development of Zeus as well as many of the lessons learned along the way. This includes details on the team's organizational structure, sensor suite, software components, and performance at the Year 2 competition. With a team of mostly undergraduates and minimal resources, aUToronto has made progress toward a functioning self‐driving vehicle, in just 2 years. This article may prove valuable to researchers looking to develop their own self‐driving platform.

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Section: Object Detection and Trackingmentioning

confidence: 72%

Zeus: A system description of the two‐time winner of the collegiate SAE autodrive competition

Burnett

Qian

et al. 2020

Journal of Field Robotics

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“…Both the X-zero and the Z-zero algorithms require the LIDAR to be in a parallel position with respect to the road surface. Although this is a common sensor setup [ 34 , 35 , 36 , 37 , 38 , 39 ] and our vehicles were equipped this way, there are notable cases where it is recommended to set it up differently. For instance, [ 38 ] has LIDAR systems both straight (parallel to the road) and tilted.…”

Section: Discussionmentioning

confidence: 99%

Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles

Horváth

Pozna

Unger

2021

Sensors

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Road and sidewalk detection in urban scenarios is a challenging task because of the road imperfections and high sensor data bandwidth. Traditional free space and ground filter algorithms are not sensitive enough for small height differences. Camera-based or sensor-fusion solutions are widely used to classify drivable road from sidewalk or pavement. A LIDAR sensor contains all the necessary information from which the feature extraction can be done. Therefore, this paper focuses on LIDAR-based feature extraction. For road and sidewalk detection, the current paper presents a real-time (20 Hz+) solution. This solution can also be used for local path planning. Sidewalk edge detection is the combination of three algorithms working parallelly. To validate the result, the de facto standard benchmark dataset, KITTI, was used alongside our measurements. The data and the source code to reproduce the results are shared publicly on our GitHub repository.

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“…For that reason, dynamic filteringbased tracking that can directly consider the physical movement characteristics is still actively used in various situations. Kalman filter-based tracking algorithms can provide an optimal solution when the motion model is modeled to a linear function [18]- [20]. Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) can estimate nonlinear motion models; however, multiple motion models are hard to consider [21]- [23].…”

Section: ) Object Trackingmentioning

confidence: 99%

Semantic Point Cloud-Based Adaptive Multiple Object Detection and Tracking for Autonomous Vehicles

Kim

Jo³

2021

IEEE Access

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LiDAR-based Multiple Object Detection and Tracking (MODT) is one of the essential tasks in autonomous driving. Since MODT is directly related to the safety of an autonomous vehicle, it is critical to provide reliable information about the surrounding objects. For that reason, we propose a semantic point cloud-based adaptive MODT system for autonomous driving. Semantic point clouds emerge with advances in deep learning-based Point Cloud Semantic Segmentation (PCSS), which assigns semantic information to each point in the point cloud of LiDAR. This semantic information provides several advantages to the MODT system. First, any point corresponding to any static object can be filtered. Because the class information assigned to each point can be directly utilized, filtering is possible without any modeling. Second, the class information of an object can be inferred without any special classification process because the class information is provided from the semantic point cloud. Finally, the clustering and tracking module can consider unique dimensional and dynamic characteristics based on class information. We utilize the Carla simulator and KITTI dataset to verify our method by comparing several existing algorithms. In conclusion, the performance of the proposed algorithm is improved by about 176% on average compared to the existing algorithm. INDEX TERMSSemantic point cloud, point cloud semantic segmentation, multiple object detection and tracking (MODT), class-adaptive tracking, autonomous vehicle, LiDAR

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aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge

Cited by 24 publications

References 22 publications

Zeus: A system description of the two‐time winner of the collegiate SAE autodrive competition

Zeus: A system description of the two‐time winner of the collegiate SAE autodrive competition

Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles

Semantic Point Cloud-Based Adaptive Multiple Object Detection and Tracking for Autonomous Vehicles

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