Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application

Coué, Christophe; Pradalier, Cédric; Laugier, Christian; Fraichard, Thierry; Bessìère, Pierre

doi:10.1177/0278364906061158

Cited by 191 publications

(152 citation statements)

References 13 publications

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“…To cope with the latter issue, hybrid environment models that combine grid-based representations of static parts with sparse, object-based representations of dynamic objects can be employed [42,73,92,104,186]. Alternatively, the grid cells' state space can be extended by velocity components [57]. 2 In recent times, grid-based representations have increasingly been used in the ADAS domain, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…The second pdf of the integrand in (3.55) is now expanded and factorized according to 57) in which the last step follows from the fact that states x k−1 at time step k − 1 do not depend on models of time step k. The so-called conditional mode probabilities or mixing probabilities µ (i|j),k−1 in (3.57) are them-selves calculated via Bayes theorem as 58) in which the abbreviation µ…”

Section: Extension Of the Optimal Bayesian Filtermentioning

confidence: 99%

“…A representative of the first category is the so-called Bayesian Occupancy Filter (BOF) [57], which generates a four-dimensional grid representation of the environment -two dimensions for the Cartesian coordinates and two for the velocity components of each cell. Each cell's motion is predicted with a constant velocity model.…”

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confidence: 99%

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Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.

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Section: Related Workmentioning

confidence: 99%

Section: Extension Of the Optimal Bayesian Filtermentioning

confidence: 99%

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Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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“…Each cell of the grid is associated with a probability of occupancy and a probability distribution on the velocity of the occupancy associated with the cell. Contrary to the initial formulation presented in Coué et al (2006), this formulation does not allow for overlapping objects. On the other hand, it allows for inferring velocity distributions and reduces the computational complexity.…”

Section: The Bayesian Occupancy Filtermentioning

confidence: 99%

Awareness of Road Scene Participants for Autonomous Driving

Petrovskaya

Perrollaz

Oliveira

et al. 2012

Handbook of Intelligent Vehicles

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This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.

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“…Using 2D grids for static indoor mapping is proposed in [11]. Other works propose multidimensional grids in which multi target tracking algorithms with obstacle state space are used with Bayesian filtering techniques [12]. Integrating perception and planning is an interesting topic.…”

Section: Introductionmentioning

confidence: 99%

WMR navigation using local potential field corridors and narrow local occupancy grid perception

Pacheco

Cufí

Luo

et al. 2008

2008 IEEE International Conference on Automation, Quality and Testing, Robotics

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-Path planning and control strategies applied to autonomous mobile robots should fulfil safety rules as well as achieve final goals. Trajectory planning applications should be fast and flexible to allow real time implementations as well as environment interactions. The methodology presented uses the on robot information as the meaningful data necessary to plan a narrow passage by using a corridor based on attraction potential fields that approaches the mobile robot to the final desired configuration. It employs local and dense occupancy grid perception to avoid collisions. The key goals of this research project are computational simplicity as well as the possibility of integrating this method with other methods reported by the research community. Another important aspect of this work consist in testing the proposed method by using a mobile robot with a perception system composed of a monocular camera and odometers placed on the two wheels of the differential driven motion system. Hence, visual data are used as a local horizon of perception in which trajectories without collisions are computed by satisfying final goal approaches and safety criteria.Index Terms -mobile robotics, local path planning, robot vision control.

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Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application

Cited by 191 publications

References 13 publications

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Awareness of Road Scene Participants for Autonomous Driving

WMR navigation using local potential field corridors and narrow local occupancy grid perception

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