Out-of-sequence measurement processing for an automotive pre-crash application

Muntzinger, Marc; Schröder, Florian; Zuther, Sebastian; Dietmayer, Klaus

doi:10.1109/itsc.2009.5309687

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…The example illustrated in Figure 4 shows an l-lag problem, where l represents the number of time steps behind the current track time [7]. The l-lag problem can be reduced to a 1-lag problem by calculating an equivalent measurement as follows [6]:…”

Section: Radar Architecture and Fusion For 360 • Coverage And Perceptionmentioning

confidence: 99%

Making Bertha See Even More: Radar Contribution

et al. 2015

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For decades, radar has been applied extensively in warfare, earth observation, rain detection, and industrial applications. All those areas are characterized by requirements such as high quality of service, reliability, robustness in harsh environment and short update time for environmental perception, and even imaging tasks. In the vehicle safety and driver assistance field, radars have found widespread application globally in nearly all vehicle brands. With the market introduction of the 2014 Mercedes-Benz S-Class vehicle equipped with six radar sensors covering the vehicles environment 360 • in the near (up to 40 m) and far range (up to 200 m), autonomous driving has become a reality even in low-speed highway scenarios. A large azimuth field of view, multimodality and a high update rate have been the key innovations on the radar side. One major step toward autonomous driving was made in August 2013. A Mercedes-Benz research S-Class vehicle-referred to at Mercedes as Bertha-drove completely autonomously for about 100 km from Mannheim to Pforzheim, Germany. It followed the well-known historic Bertha Benz Memorial Route. This was done on the basis of one stereo vision system, comprising several long and short range radar sensors. These radars have been modified in Doppler resolution and dramatically improved in their perception capabilities. The new algorithms consider that urban scenarios are characterized by significantly shorter reaction and observation times, shorter mean free distances, a 360 • interaction zone, and a large variety of object types to be considered. This paper describes the main challenges that Daimler radar researchers faced and their solutions to make Bertha see.INDEX TERMS Radar, automotive radar, autonomous driving.

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Section: Radar Architecture and Fusion For 360 • Coverage And Perceptionmentioning

confidence: 99%

Making Bertha See Even More: Radar Contribution

et al. 2015

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“…The optimal solution to the out-of-sequence problem was introduced by Bar-Shalom [1], alternative methods were proposed in [6] and [19]. They were first applied in automotive contexts by Muntzinger [11], [10] with a special focus on pre-crash applications in [8].…”

Section: Related Workmentioning

confidence: 99%

Impact of out-of-sequence measurements on the joint integrated probabilistic data association filter for vehicle safety systems

Westenberger

Duraisamy

Münz

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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This paper addresses the problem of joint state and existence estimation in the presence of temporally asynchronous measurements. In multi-sensor fusion, the problem can occur that measurements from different sensors can arrive at the processing unit out of sequence, i.e., the original temporal order of the measurements is lost. For the first time, the influence of these out-of-sequence measurements on state estimation as well as on existence estimation is examined. The existence probabilities are estimated via the Joint Integrated Probabilistic Data Association Filter (JIPDA) [17]. Two different methods to deal with out-of-sequence measurements in JIPDA are described and compared. It is shown that the handling of out-of-sequence measurements has a considerable influence not only on state, but also on existence estimation.

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“…which can be fully constructed similarly to (7). The second term on the right hand side of (9) has to be obtained recursively, i.e.,…”

Section: B the Bayesian Approachmentioning

confidence: 99%

“…which can be obtained similarly to (7). The initial value of p(xn|x k , z 1:k−1 ) in recursion (11) is p(x k−1 |x k , z 1:k−1 ), obtained by substituting n = k − 1 into (12).…”

Section: B the Bayesian Approachmentioning

confidence: 99%

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Particle filter processing of out-of-sequence measurements: Exact Bayesian solution

Zhang¹,

Bar‐Shalom

2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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This paper considers the problem of out-of-sequence measurement (OOSM) processing when the filtering technique used at the tracker is a particle filter (PF). First, the exact Bayesian algorithm for updating with OOSMs is derived. Then, the PF implementation of the exact Bayesian algorithm, called A-PF, is developed. Since A-PF is rooted in exact Bayesian inference, if the number of particles is sufficiently large, A-PF is the one (and the only one) that is able to achieve the optimal performance obtained from the in-sequence processing. This is confirmed by the simulation results. Also, it is shown that the performance of A-PF is always superior to previous (heuristic) PF-based algorithms with the same number of particles.Index Terms-Particle filter, Bayesian estimation, out-of-sequence measurement (OOSM)

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Out-of-sequence measurement processing for an automotive pre-crash application

Cited by 5 publications

References 17 publications

Making Bertha See Even More: Radar Contribution

Making Bertha See Even More: Radar Contribution

Impact of out-of-sequence measurements on the joint integrated probabilistic data association filter for vehicle safety systems

Particle filter processing of out-of-sequence measurements: Exact Bayesian solution

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