Providentia – A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

Krämmer, Annkathrin; Schöller, Christoph; Gulati, Dhiraj; Lakshminarasimhan, Venkatnarayanan; Kurz, Franz; Rosenbaum, Dominik; Lenz, Claus; Knoll, Alois

doi:10.55417/fr.2022038

Cited by 23 publications

(11 citation statements)

References 32 publications

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“…Note that the evaluation data in [4] was synthesized and only covered a small intersection area. An experimental setup similar to ours, where trajectories were recorded from a gantry bridge, is described by Krämmer et al [38]. Compared to their work, the RMSE values are lower by 63.5 % for the overall position, 66.5 % for the longitudinal position, and 35.4 % for the lateral position.…”

Section: B Accuracy Evaluation 1) Time Synchronizationmentioning

confidence: 64%

MONA: The Munich Motion Dataset of Natural Driving

Gressenbuch

Esterle

Keßler

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Real-world datasets facilitate the development of autonomous vehicles, especially when they are accessible, diverse, and provide a measure of accuracy. While existing datasets have been accessible and diverse, they cannot provide any measure of accuracy. To estimate the accuracy of the detection of traffic participants in our setup, we repetitively drove through our observation area with a measurement vehicle with highly accurate localization and LiDAR sensors. Our experiments showed an average overall position accuracy of 0.51 m. The combined data of the autonomous vehicle and the elevated camera setup yield a unique dataset. The elevated view acts as a super sensor of the autonomous vehicle with extended range and reduced occlusions. We employ an auto-labeling system on the stationary camera data to extract trajectories with bounding boxes for each traffic participant. These extracted trajectories are smoothed for kinematic feasibility, and corresponding maps for each location are provided. The Munich Motion Dataset of Natural Driving (MONA) shall empower new research in prediction and planning. Making raw data and code available to the public without license restrictions allows the dataset to be further improved using more advanced algorithms.

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Section: B Accuracy Evaluation 1) Time Synchronizationmentioning

confidence: 64%

MONA: The Munich Motion Dataset of Natural Driving

Gressenbuch

Esterle

Keßler

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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“…Therefore, we create our TUMTraf Event Dataset, which includes spatiotemporal synchronized frame triplets of event-based, RGB, and combined RGB-event-based images, see Table II. For this, we first record the raw data with our ITS from the Providentia++ project [65], [66], which contains event-based and RGB cameras. As illustrated in Figure 3, the sensors are set up on a gantry at a height of 7 m located at an intersection in Garching near Munich, Germany.…”

Section: B Datasetmentioning

confidence: 99%

TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras

Creß,

Zimmer,

Purschke

et al. 2024

IEEE Trans. Intell. Veh.

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Event-based cameras are predestined for Intelligent Transportation Systems (ITS). They provide very high temporal resolution and dynamic range, which can eliminate motion blur and improve detection performance at night. However, eventbased images lack color and texture compared to images from a conventional RGB camera. Considering that, data fusion between event-based and conventional cameras can combine the strengths of both modalities. For this purpose, extrinsic calibration is necessary. To the best of our knowledge, no targetless calibration between event-based and RGB cameras can handle multiple moving objects, nor does data fusion optimized for the domain of roadside ITS exist. Furthermore, synchronized event-based and RGB camera datasets considering roadside perspective are not yet published. To fill these research gaps, based on our previous work, we extended our targetless calibration approach with clustering methods to handle multiple moving objects. Furthermore, we developed an early fusion, simple late fusion, and a novel spatiotemporal late fusion method. Lastly, we published the TUMTraf Event Dataset, which contains more than 4,111 synchronized event-based and RGB images with 50,496 labeled 2D boxes. During our extensive experiments, we verified the effectiveness of our calibration method with multiple moving objects. Furthermore, compared to a single RGB camera, we increased the detection performance of up to +9% mAP in the day and up to +13% mAP during the challenging night with our presented event-based sensor fusion methods. The TUMTraf Event Dataset is available at https://innovationmobility.com/tumtraf-dataset.

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“…Because of the motivating objectives of closed course testbeds, they can be limited in their ability to test in real traffic conditions with regular drivers encountered on public roads. Open road testbeds exist in many forms on a variety of road types; examples include the Minnesota Traffic Observatory [54], The Ray [55], the California Connected Vehicle Test Bed [56], Ann Arbor Connected Vehicle Test Environment [57], and Providentia [58]. They support experiments in live traffic, similar to the I-24 instrument.…”

Section: B Existing Testbedsmentioning

confidence: 99%

I-24 MOTION: An instrument for freeway traffic science

Gloudemans¹,

Wang²,

Ji³

et al. 2023

Preprint

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The Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION) is a new instrument for traffic science located near Nashville, Tennessee. I-24 MOTION consists of 276 pole-mounted high-resolution traffic cameras that provide seamless coverage of approximately 4.2 miles I-24, a 4-5 lane (each direction) freeway with frequently observed congestion. The cameras are connected via fiber optic network to a compute facility where vehicle trajectories are extracted from the video imagery using computer vision techniques. Approximately 230 million vehicle miles of travel occur within I-24 MOTION annually. The main output of the instrument are vehicle trajectory datasets that contain the position of each vehicle on the freeway, as well as other supplementary information, vehicle dimensions, and class. This article describes the design and creation of the instrument, and provides the first publicly available datasets generated from the instrument. The datasets published with this article contains at least 4 hours of vehicle trajectory data for each of 10 days. As the system continues to mature, all trajectory data will be made publicly available at i24motion.org.

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Providentia – A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

Cited by 23 publications

References 32 publications

MONA: The Munich Motion Dataset of Natural Driving

MONA: The Munich Motion Dataset of Natural Driving

TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras

I-24 MOTION: An instrument for freeway traffic science

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