Probability estimation for an automotive Pre-Crash application with short filter settling times

Muntzinger, Marc; Zuther, Sebastian; Dietmayer, Klaus

doi:10.1109/ivs.2009.5164313

Cited by 14 publications

(13 citation statements)

References 10 publications

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“…which can be known from (14). As mentioned in remark 7, the normal distribution of TTC estimate has narrow width, and thus it enables the pdf estimation of y hit based on only a very small number of Gaussian components satisfying highly efficient performance.…”

Section: Gaussian Mixture Based Collision Probabilitymentioning

confidence: 99%

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Gaussian mixture approach to decision making for automotive collision warning systems

Han

Sang

Whang

et al. 2015

Int. J. Control Autom. Syst.

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This paper proposes a practical probabilistic approach to collision decision making which is necessary for advanced automotive collision warning system (CWS) using FMCW radar. Most decision making algorithms assess the probable collisions based on the predicted collision position which is usually expressed as a nonlinear function of threat vehicle's position and velocity provided by FMCW radar. Since the predicted collision position has highly nonlinear statistics in general, it is one of main obstacles to improving the reliability of the collision probability computation and to developing real-time decision making algorithms. This motivates us to devise a Gaussian mixture method for collision probability calculation with the help of linear recursive time-to-collision (TTC) estimation. The suggested TTC estimator provides an accurate TTC estimate with small estimation error variance hence it enables us to approximate the probability density function of the predicted collision position as the weighted sum of just a few Gaussian distributions. Therefore, our approach could drastically reduce the inherent nonlinearity of collision decision making problem and computational complexity in collision probability calculation. Through the simulations for the typical engagement scenarios between the host and threat vehicles, the performance and effectiveness of the proposed algorithm is compared to those of the existing ones which require heavy computational burden.

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Section: Gaussian Mixture Based Collision Probabilitymentioning

confidence: 99%

“…For collision warning, this approach adopts the probability density function (pdf) of a collision position predicted by the current target state [14][15][16][17][18]. It has advantages over the TTC based schemes because it can provide mathematically rigorous way to define collision probability as a threat measure.…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture approach to decision making for automotive collision warning systems

Han

Sang

Whang

et al. 2015

Int. J. Control Autom. Syst.

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“…A PreCrash system is designed to detect a possibility of a collision and is typically used to prepare the vehicle for an imminent impact by pre-tensioning seat belts or taking other actions [11,12]. While this system may also be used to warn drivers, similarly to the EEBL application, since it typically only relies on knowledge of the vehicle ahead, it may not provide warnings in sufficient time for driver to take action.…”

Section: Relevant Workmentioning

confidence: 99%

Intelligent transportation systems: When is safety information relevant?

Szczurek

Wolfson

et al. 2011

2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

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Abstract-In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application. One uses an analytically derived formula based on the minimal safety gap required to avoid a collision. The other method uses a machine learning approach. The application works by disseminating reports about vehicles that are performing emergency deceleration in effort to warn drivers about the need to perform emergency braking. Vehicles which receive such reports have to decide whether the information contained in the report is relevant to the driver, and warn the driver if that is the case. Common ways to determine relevance are based on the lane or direction information, but using only these attributes can still lead to many false warnings, which can desensitize the driver. Desensitized drivers may ignore warnings or turn off the system completely, thus eliminating any safety benefits of the application. We show that the machine learning method, in comparison to the analytically derived formula, is able to significantly reduce the number of false warnings by learning from the actions drivers take after receiving a report. The methods were compared using simulated experiments with a range of traffic and communication parameters.

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“…In order to determine whether an object poses an imminent threat of a crash to the vehicle, the object's track state and covariance is projected forward in time and space to predict the object's impact point and time-to-collision (TTC), as presented in [12]. Using this information, an active saftey system can trigger a mechanical system to protect the passenger, such as a belt-pretensioner, brakes, etc.…”

Section: Automotive Pre-crash Applicationmentioning

confidence: 99%

Tracking in a cluttered environment with out-of-sequence measurements

Muntzinger

Aeberhard

Schröder

et al. 2009

2009 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

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Recent literature has introduced several methods for processing an out-of-sequence measurement (OOSM) in tracking for both the 1-step-lag and l-step-lag case. However, in a realistic tracking application, a data association algorithm, such Joint Probabilistic Data Association (JPDA) must be used in order to associate measurements with tracks in a cluttered environment. This paper investigates the OOSM problem in tracking for both the 1-step-lag and l-step-lag cases using JPDA. The OOSM algorithms presented in the literature are extended to support JPDA and the results are analyzed. The resulting algorithms are then applied to an automotive frontal pre-crash system, where OOSM and JPDA are vital factors in improving the performance of such a system.

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Probability estimation for an automotive Pre-Crash application with short filter settling times

Cited by 14 publications

References 10 publications

Gaussian mixture approach to decision making for automotive collision warning systems

Gaussian mixture approach to decision making for automotive collision warning systems

Intelligent transportation systems: When is safety information relevant?

Tracking in a cluttered environment with out-of-sequence measurements

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