Performance bounds on change detection with application to manoeuvre recognition for advanced driver assistance systems

Stellet, Jan Erik; Schumacher, Jan; Branz, Wolfgang; Zöllner, J. Marius

doi:10.1109/ivs.2015.7225833

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…As in previous works [4], [30], we process the noisy tracks using the Rauch-Tung-Striebel (RTS) smoother [31]. Schubert et al [32] investigated the performance of different vehicle models for vehicle tracking applications.…”

Section: Smoothingmentioning

confidence: 99%

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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: Smoothingmentioning

confidence: 99%

“…The state of our vehicle model is defined as x = (s x , s y , v, ψ, ω) T , where s x , s y denote the x-and yposition of the vehicle, ψ the heading, v the velocity, and ω the yaw rate. The discrete-time dynamic model of our vehicle is [30] x…”

Section: Smoothingmentioning

confidence: 99%

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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show abstract

“…A popular post-processing approach on the object level is to initialize objects individually at a well observable time step and to track them for both past and future times. This is shown for classical fitting of L-, I-, and U-shapes in [4] and for computer-aided design (CAD) models in [5].…”

Section: A Related Workmentioning

confidence: 99%

Offline Dynamic Grid Generation for Automotive Environment Perception Using Temporal Inference Methods

Würsching

Steyer

et al. 2021

IEEE Robot. Autom. Lett.

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The validation of online perception algorithms in automotive systems requires a large amount of ground-truth data. Since manual labeling is inefficient and error-prone, an automatic generation of accurate and reliable reference data is desirable. We present a post-processing approach based on a particlebased dynamic occupancy grid representation of the environment. In contrast to existing online dynamic grid algorithms, our estimation additionally utilizes future measurements by applying offline smoothing algorithms. Our proposed concept uses a twofilter procedure for smoothing the occupancy states of the grid cells. We further introduce two methods based on particle reweighting and two-filter smoothing to improve the velocity estimates. We show that our approach enhances the situational awareness and thus provides a more precise environment model. We demonstrate these benefits using lidar data from real-world experiments.

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