A Multilevel Collision Mitigation Approach&amp;amp;mdash;Its Situation Assessment, Decision Making, and Performance Tradeoffs

Hillenbrand, J.; Spieker, A.; Kroschel, Kristian

doi:10.1109/tits.2006.883115

Cited by 183 publications

(101 citation statements)

References 16 publications

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“…[17], [30]. Another possibility to predict traffic situations is to simulate single behaviors of traffic participants [10], [15], resulting in measures like time to collision or predicted minimum distance. Due to the efficiency of single simulations, these approaches are already widely implemented in cars.…”

mentioning

confidence: 99%

Model-Based Probabilistic Collision Detection in Autonomous Driving

Althoff

Stursberg

Buss

2009

IEEE Trans. Intell. Transport. Syst.

267

174

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Abstract-Safety of planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Thereto, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants as well as the limitation of driving maneuvers due to the road geometry is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most intensive computations are performed offline, resulting in a lean online algorithm for real-time application.

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mentioning

confidence: 99%

Model-Based Probabilistic Collision Detection in Autonomous Driving

Althoff

Stursberg

Buss

2009

IEEE Trans. Intell. Transport. Syst.

267

174

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“…The most prominent representative of this type is the Time-To-Collision (TTC), which quantifies the remaining time until a collision happens given an assumed dynamic prediction model. 8 Other time metrics are the Time-To-React (TTR) [101,242] (Fig. 6.5a), and its approximations via Time-To-Brake (TTB), Time-To-Kickdown (TTK), and Time-To-Steer (TTS) introduced in [101] and used in [214] for overtaking situations.…”

Section: Criticality Assessmentmentioning

confidence: 99%

“…8 Other time metrics are the Time-To-React (TTR) [101,242] (Fig. 6.5a), and its approximations via Time-To-Brake (TTB), Time-To-Kickdown (TTK), and Time-To-Steer (TTS) introduced in [101] and used in [214] for overtaking situations. In contrast to TTC, the TTR corresponds to the time a driver has left to start a maneuver that circumvents a collision.…”

Section: Criticality Assessmentmentioning

confidence: 99%

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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“…In general, these algorithms try to predict the future state of the involved vehicles in order to estimate the effort needed to avoid an accident [4], [5], [6]. Previously proposed solutions range from rather simple methods, measuring the collision risk in terms of the time to collision (TTC) [7], the predicted minimum distance [8] or the required deceleration [9], to model predictive control [10] and path planning [11], [12]. Among others, [13] proposes a lane change maneuver formulated as an optimization problem with a point mass vehicle model (for fast computation), [14] presents an optimal trajectory generator minimizing the yaw acceleration, while [15], [16] show a collision warning system based on two meaningful indices, i.e., the steering threat number (STN) and braking threat number (BTN).…”

Section: Introductionmentioning

confidence: 99%

Adaptive forward collision warning algorithm for automotive applications

Hosseini

Murgovski

Campos

et al. 2016

2016 American Control Conference (ACC)

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Abstract-This paper proposes an adaptive collision warning algorithm (CWA) that supports the driver by issuing early warnings for collision avoidance without noticeably increasing the risk of false alarms in real traffic. This algorithm can also detect when an emergency intervention is necessary. Compared to existing CWAs, the proposed solution in this paper triggers alarms by solving a linear convex program using traffic data, road's constraints and bicycle model dynamics, and by incorporating an adaptive (speed-dependent) warning threshold. A collision threat is detected by determining feasible steering trajectories without altering the vehicle's longitudinal velocity.

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A Multilevel Collision Mitigation Approach&mdash;Its Situation Assessment, Decision Making, and Performance Tradeoffs

Cited by 183 publications

References 16 publications

Model-Based Probabilistic Collision Detection in Autonomous Driving

Model-Based Probabilistic Collision Detection in Autonomous Driving

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

Adaptive forward collision warning algorithm for automotive applications

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