Multi-sensor data fusion in automotive applications

Herpel, Thomas; Lauer, Christoph; German, Reinhard; Salzberger, J.

doi:10.1109/icsenst.2008.4757100

Cited by 21 publications

(9 citation statements)

References 3 publications

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“…Then, a three-step filtering module estimates the new parameters of the detected objects, updates the tracking and evaluates their future parameters. When using several sensors, a fusion step can be added at the signal processing level, after the detection level, or at the end of the processing unit [15], [16].…”

Section: Systemmentioning

confidence: 99%

Model-based detection and tracking of vehicle using a scanning laser rangefinder: A particle filtering approach

Vanpoperinghe¹,

Wahl²,

Noyer³

2012

2012 IEEE Intelligent Vehicles Symposium

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A method derived from the Sequential Monte Carlo approaches is proposed here to solve the vehicle detection and tracking problem using a scanning laser rangefinder. The originality of this approach lies in a joint detection and tracking of the objects that avoid the usual pre-detection stage. The proposed modeling is strongly nonlinear. To improve the efficiency of the solution, we use a Rao-Blackwell particle filter: the non-linearity of the state-space equations is taken into account by a particle filter and the linearity is optimally processed by a Kalman filter. The solution of the proposed modeling is based on a matched filter (to the object) which uses a predefined vehicle model. A central point here is to calculate the weights of the matched particle filter according to the vehicle model. The efficiency of the method is shown in terms of estimation accuracies and detection.

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Section: Systemmentioning

confidence: 99%

Model-based detection and tracking of vehicle using a scanning laser rangefinder: A particle filtering approach

Vanpoperinghe¹,

Wahl²,

Noyer³

2012

2012 IEEE Intelligent Vehicles Symposium

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“…Multi-sensor fusion is the process of combining data from multiple sensors so that the cumulative data are enhanced in terms of reliability, consistency, and quality, compared to the data that would be acquired from a single sensor [ 12 , 13 ]. In this paper, we focus on ‘object-level’ or ‘high-level’ multi-sensor fusion techniques for emergency brake assist (EBA) systems.…”

Section: Introductionmentioning

confidence: 99%

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

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“…Although the paper only focuses on the traffic about the road, but a type of multi-source data fusion technology was described. However there are also some difficulties to be solved including accurate demand, real time and dynamic performance and the guarantee for data quality [9].A new type of algorithm is designed by improving the kalman filter algorithm according to the characteristics of the freeway and urban expressway [10]. A fusion algorithm for multi-source data is designed and verified by experiments [11].Chou [12] designed a data model including different module function, which can improve the precision of the fusion with their interaction.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-source Traffic Flow Data Fusion Based on Linear Regression: A Case Study in Mega-city

Wang¹,

Shao²,

Ji³

et al. 2016

Proceedings of the 2nd Annual International Conference on Advanced Material Engineering (AME 2016)

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Abstract. Data fusion of traffic parameters such as speed and density is a crucial study in intelligent transportation system, yet complicated to formulate mathematically. The aim of this paper is to study and model the multi-source fusion traffic flow data. Based on the data detected in a mega-city, the regression model is developed to solve the defects in the traditional models in this paper. The result demonstrates that the proposed models can pass the effectiveness test and have a favorable fusion effect, whose fusion result can meet the demand for precision. Apart from the accuracy demand, the proposed models can solve the fusion problem briefly and effectively so that it can be used in the practical engineering application. This study concludes that the proposed model has expressed well the data fusion for the practical multi-source data detected in the mega-city, which proves the model has good effectiveness and applicability.

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Multi-sensor data fusion in automotive applications

Cited by 21 publications

References 3 publications

Model-based detection and tracking of vehicle using a scanning laser rangefinder: A particle filtering approach

Model-based detection and tracking of vehicle using a scanning laser rangefinder: A particle filtering approach

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Research on Multi-source Traffic Flow Data Fusion Based on Linear Regression: A Case Study in Mega-city

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