Reliable automotive pre-crash system with out-of-sequence measurement processing

Muntzinger, Marc; Aeberhard, Michael; Zuther, Sebastian; Mahlisch, Mirko; Schmid, Matthias R.; Dickmann, Jürgen; Dietmayer, Klaus

doi:10.1109/ivs.2010.5548149

Cited by 29 publications

(14 citation statements)

References 6 publications

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“…The problem of precise time-stamping of sensor data was further investigated in [8]. The problem of how to process asynchronous and out-of-sequence data in a low-level fusion architecture was shown in [9] for a pre-crash application with radar sensors. In high-level sensor data fusion architectures for automotive applications, sensor tracks are usually directly fused with one another and synchronized by predicting the sensor tracks to a common time before fusion [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Track-to-track fusion with asynchronous sensors and out-of-sequence tracks using information matrix fusion for advanced driver assistance systems

Aeberhard

Rauch

Rabiega

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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Future advanced driver assistance systems will contain multiple sensors that are used for several applications, such as highly automated driving on freeways. The problem is that the sensors are usually asynchronous and their data possibly out-of-sequence, making fusion of the sensor data non-trivial. This paper presents a novel approach to trackto-track fusion for automotive applications with asynchronous and out-of-sequence sensors using information matrix fusion. This approach solves the problem of correlation between sensor data due to the common process noise and common track history, which eliminates the need to replace the global track estimate with the fused local estimate at each fusion cycle. The information matrix fusion approach is evaluated in simulation and its performance demonstrated using real sensor data on a test vehicle designed for highly automated driving on freeways.

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Section: Introductionmentioning

confidence: 99%

Track-to-track fusion with asynchronous sensors and out-of-sequence tracks using information matrix fusion for advanced driver assistance systems

Aeberhard

Rauch

Rabiega

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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“…The main development steps today have been the introduction of multimodality in one sensor, digital beam forming combined with electronically steering and architectural changes to achieve improved Doppler resolution. Pre-Crash systems require a high angular accuracy, a wide FoV and a very fast up-date rate of few 10´s ms and small latency [4]. In order to allow proper radar operation for Emergency braking systems also in curves, driving lane prediction is an essential feature [5].…”

Section: A Radar Functions From History To Todaymentioning

confidence: 99%

"Present research activities and future requirements on automotive radar from a car manufacturer´s point of view"

Dickmann

Klappstein

Hahn

et al. 2015

2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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Automotive Radar has already found its way into nearly all car manufacturers portfolio, even for small car platforms. Over the decades, the performance requirements increased steadily from simple detector tasks in blind spot monitoring systems to multi range smart environment perception sensors. The utmost push in performance requirement is initiated with the trend towards highly automated driving. Future automotive radar systems have to provide imaging like capabilities and have to interact in radar networks, which allow for highly comprehensive perception tasks. The paper will provide an overview on state of the art automotive radar usage on the basis of the DAIMLER car platforms, will give an outline on future requirements for highly automated driving and will present recent approaches in radar based environmental perception.

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“…Thus, a forward prediction of the CAM solely based on the position, the heading and the velocity for a time span of up to 800 ms will generate rather inaccurate results [23]. Therefore, we decided to use a buffering strategy as explained in [24] to generate first baseline results which can be used later on for further evaluations and comparisons. Due to the fact that the used ITS-G5 devices derive the vehicle dynamics from an attached low-cost GPS device and do not apply any filtering refinement, the CAMs are incorporated by measurement-to-track fusion.…”

Section: Time Synchronization and Fusion Schemamentioning

confidence: 99%

Multi-sensor data fusion for checking plausibility of V2V communications by vision-based multiple-object tracking

Obst¹,

Hobert

Reisdorf

2014

2014 IEEE Vehicular Networking Conference (VNC)

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Automated vehicles require an adequate and reliable perception of the surrounding world in order to make good decisions. Using vehicle-to-vehicle (V2V) communication to exchange location data (i.e. time, position, heading and speed) can improve the perception beyond the capabilities of traditional on-board sensors (e.g. radar, lidar). However, it is vital to trust the data before it is being used. Cryptographic mechanisms can protect the exchange and authenticity of data but do not guarantee the correctness of the content. In this paper we present a vision-based multi-object tracking system for checking the plausibility of V2V communication. The system is addressing the challenge of fusing relative sensor observations as provided by a MobilEye vision-system with time-delayed absolute GNSS-based measurements from Cooperative Awareness Messages (CAMs) as provided by V2V. The plausibility check is implemented in a prototype and based on a state-of-the-art multiple-object tracking algorithm. The proposed system is evaluated and validated under real-world conditions by conducting several test drives under urban conditions.

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Reliable automotive pre-crash system with out-of-sequence measurement processing

Cited by 29 publications

References 6 publications

Track-to-track fusion with asynchronous sensors and out-of-sequence tracks using information matrix fusion for advanced driver assistance systems

Track-to-track fusion with asynchronous sensors and out-of-sequence tracks using information matrix fusion for advanced driver assistance systems

"Present research activities and future requirements on automotive radar from a car manufacturer´s point of view"

Multi-sensor data fusion for checking plausibility of V2V communications by vision-based multiple-object tracking

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