High level sensor data fusion for automotive applications using occupancy grids

García, Ramón Albajes; Aycard, Olivier; Vu, Trung-Dung; Ahrholdt, Malte

doi:10.1109/icarcv.2008.4795574

Cited by 14 publications

(16 citation statements)

References 9 publications

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“…This architecture has been used in the european project PReVENT-ProFusion2 in cooperation with Daimler [1] and also with Volvo Truck [5] (not presented here). It has been also used with CMU demonstrator [2], and is currently used in the european project intersafe2 [3].…”

Section: Discussionmentioning

confidence: 99%

An Occupancy Grid Based Architecture for ADAS

Aycard

Baig

2010

Advanced Microsystems for Automotive Applications 2010

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Perceiving or understanding the environment surrounding a vehicle is a very important step in advanced driving assistance systems (ADAS). The task involves both Simultaneous Localization And Mapping (SLAM) and Detection And Tracking of Moving Objects (DATMO). In this context, we have developed a generic architecture based on occupancy grid to solve SLAM and DATMO in dynamic outdoor environments. In this paper, we give an overview of this architecture and results obtained in different european projects: PReVENT-ProFusion2, INTERSAFE2 & Interactive.

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Section: Discussionmentioning

confidence: 99%

An Occupancy Grid Based Architecture for ADAS

Aycard

Baig

2010

Advanced Microsystems for Automotive Applications 2010

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“…The three regions are clearly visible in Fig 3.12, which shows an exemplary inverse sensor model of a 2D range sensor based on [77,86,269]. In front of the obstacle, the inverse sensor model is described by a linear, rising function, which stays below an occupancy value of 0.5.…”

Section: Inverse Sensor Modelsmentioning

confidence: 99%

“…learning [247], derived analytically from the forward sensor model in special cases [77,145], or specified manually directly [86,269]. For 2D range sensors, it is commonly assumed that the sensor outputs a noisy range and angle measurement of a detected obstacle.…”

Section: Inverse Sensor Modelsmentioning

confidence: 99%

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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“…The goal of this project was to design and develop generic architectures to perform perception tasks (i.e., mapping of the environment, localization of the vehicle in the map, and detection and tracking of moving objects). In this context, our architecture has been integrated and tested on two demonstrators: a Daimler-Mercedes demonstrator [25] and a Volvo Truck demonstrator [10]. The main difference between these 2 demonstrators is the level of abstraction of data provided by the different sensors on each demonstrator: raw data for the Daimler-Mercedes demonstrator (i.e., low level of abstraction) and preprocessed data for the Volvo Truck demonstrator (i.e., high level of abstraction).…”

Section: Introductionmentioning

confidence: 99%

Online localization and mapping with moving objects detection in dynamic outdoor environments

Baig

Aycard

2009

2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing

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International audienceIn this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on dataset collected from INTERSAFE-2 demonstrator for typical scenario show the effectiveness of this technique

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High level sensor data fusion for automotive applications using occupancy grids

Cited by 14 publications

References 9 publications

An Occupancy Grid Based Architecture for ADAS

An Occupancy Grid Based Architecture for ADAS

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

Online localization and mapping with moving objects detection in dynamic outdoor environments

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