Free Space Detection: A Corner Stone of Automated Driving

Neumann, Leo; Vanholme, Benoit; Gressmann, Markus; Bachmann, Alexander; Kahlke, Lars; Schule, Florian

doi:10.1109/itsc.2015.210

Cited by 11 publications

(9 citation statements)

References 15 publications

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Section: Ground-obstacle Segmentationmentioning

confidence: 99%

“…Automated vehicle control may actually induce safety risks when relevant information is missing [29]. In a given scenario, ground-plane detection based on a stereo-vision system may contribute to improved high-level driver assistance functionality [29]. Major challenges for this functionality may appear due to arising deviations which may require confidence estimation in parallel to the supported decision process [24].…”

Section: Ground-obstacle Segmentationmentioning

confidence: 99%

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Extending the stixel world using polynomial ground manifold approximation

Saleem

Rezaei

Klette

2017

2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

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Stixel-based segmentation is specifically designed towards obstacle detection which combines road surface estimation in traffic scenes, stixel calculations, and stixel clustering. Stixels are defined by observed height above road surface. Road surfaces (ground manifolds) are represented by using an occupancy grid map. Stixel-based segmentation may improve the accuracy of real-time obstacle detection, especially if adaptive to changes in ground manifolds (e.g. with respect to non-planar road geometry). In this paper, we propose the use of a polynomial curve fitting algorithm based on the v-disparity space for ground manifold estimation. This is beneficial for two reasons. First, the coordinate space has inherently finite boundaries, which is useful when working with probability densities. Second, it leads to reduced computation time. We combine height segmentation and improved ground manifold algorithms together for stixel extraction. Our experimental results show a significant improvement in the accuracy of the ground manifold detection (an 8% improvement) compared to occupancy-grid mapping methods.

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Section: Ground-obstacle Segmentationmentioning

confidence: 99%

Section: Ground-obstacle Segmentationmentioning

confidence: 99%

Extending the stixel world using polynomial ground manifold approximation

Saleem

Rezaei

Klette

2017

2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

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“…Recently, various studies on autonomous vehicles have been conducted in various fields. There are papers on various signal processing methods of LiDAR sensors mainly used in autonomous vehicles [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Among them, there are many papers that presented research on free space sensing.…”

Section: Introductionmentioning

confidence: 99%

“…Free space detection is being studied not only for the study of autonomous vehicle driving, but also in various fields such as the recognition of a parking space or the driving of an aircraft [ 25 , 26 , 27 ]. Among them, the research trends in the detection of free space in autonomous vehicles are as follows [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Often, a camera or LiDAR sensor is used, or both sensors are used, to recognize the space and obstacles around the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…J. Tao [ 3 ] used LiDAR sensor data to detect free space, in which free space was also detected without information about the speed of the obstacle. Many previous studies used static data without considering the speed of obstacles [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Free space detection for autonomous driving must also be considered in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

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Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Lee

You

2021

Sensors

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In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.

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