Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling

Eidehall, Andreas; Petersson, Lars

doi:10.1109/tits.2007.909241

Cited by 162 publications

(110 citation statements)

References 10 publications

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“…• Methods that fuse dynamic motion models with behavior and environment descriptions [5,45,75,82,88,144,211,236].…”

Section: Long-term Trajectory Predictionmentioning

confidence: 99%

“…The approaches of [45,75] perform probabilistic trajectory predictions by employing path-planning algorithms from the viewpoint of each traffic participant to generate distributions over future motions of all vehicles. Different combinations of future system inputs are considered via Monte Carlo simulations, in which the stochastic inputs are restricted to specific, typical human driving behaviors and actions such as lane changes or overtaking.…”

Section: Long-term Trajectory Predictionmentioning

confidence: 99%

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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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“…• Methods that fuse dynamic motion models with behavior and environment descriptions [5,45,75,82,88,144,211,236].…”

Section: Long-term Trajectory Predictionmentioning

confidence: 99%

Section: Long-term Trajectory Predictionmentioning

confidence: 99%

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

Schreier

2017

At - Automatisierungstechnik

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“…Like [15], this framework is based on the postulate that drivers will not perform maneuvers with high collision risks if safer options are possible. As shown before, the common approach to situation prediction implies independent prediction of each traffic participant.…”

Section: Problem Statementmentioning

confidence: 99%

“…Monte Carlo sampling is used to find an approximate solution for several trajectories. Further [15] obtains a posterior distribution of the future inputs, assuming that drivers try to avoid collisions. With this posterior distribution, threat assessment can be done.…”

Section: Introductionmentioning

confidence: 99%

Interactive scene prediction for automotive applications

Lawitzky

Althoff

Passenberg

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this work, a framework for motion prediction of vehicles and safety assessment of traffic scenes is presented. The developed framework can be used for driver assistant systems as well as for autonomous driving applications. In order to assess the safety of the future trajectories of the vehicle, these systems require a prediction of the future motion of all traffic participants. As the traffic participants have a mutual influence on each other, the interaction of them is explicitly considered in this framework, which is inspired by an optimization problem. Taking the mutual influence of traffic participants into account, this framework differs from the existing approaches which consider the interaction only insufficiently, suffering reliability in real traffic scenes. For motion prediction, the collision probability of a vehicle performing a certain maneuver, is computed. Based on the safety evaluation and the assumption that drivers avoid collisions, the prediction is realized. Simulation scenarios and real-world results show the functionality.

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“…The second step consists in checking these trajectories for intersection points. Numerous algorithms rely on a physical model of vehicles to do that [8], [9], [10], but they are not able to reason on a high-level basis about a situation and therefore are limited to short-term collision prediction. Other approaches estimate the maneuver intention of the drivers to better predict trajectories in the long term.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment at road intersections: Comparing intention and expectation

Lefèvre

Laugier

Ibañez‐Guzmán

2012

2012 IEEE Intelligent Vehicles Symposium

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Abstract-Intersections are the most complex and hazardous areas of the road network, and 89% of accidents at intersection are caused by driver error. We focus on these accidents and propose a novel approach to risk assessment: in this work dangerous situations are identified by detecting conflicts between intention and expectation, i.e. between what drivers intend to do and what is expected of them. Our approach is formulated as a Bayesian inference problem where intention and expectation are estimated jointly for the vehicles converging to the same intersection. This work exploits the sharing of information between vehicles using V2V wireless communication links. The proposed solution was validated by field experiments using passenger vehicles. Results show the importance of taking into account interactions between vehicles when modeling intersection situations.

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Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling

Cited by 162 publications

References 10 publications

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

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

Interactive scene prediction for automotive applications

Risk assessment at road intersections: Comparing intention and expectation

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