Joint road geometry estimation and vehicle tracking

Eidehall, Andreas; Pohl, Jochen; Gustafsson, Fredrik

doi:10.1016/j.conengprac.2007.02.010

Cited by 42 publications

(31 citation statements)

References 10 publications

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“…Some other approximations of the transformation T are described in [50]. Since the transformation is nonlinear, the unscented transform, see e.g., [51], is used to propagate the Gaussian components between the two spaces.…”

Section: Road Mappingmentioning

confidence: 99%

Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Lundquist

Hammarstrand

Gustafsson

2011

IEEE Trans. Signal Process.

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Abstract-Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into different clusters, which structures the description of the prior and considerably improves the measurement update in the PHD filter. Second, a merging step is proposed to simplify the map representation in the PHD filter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.

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Section: Road Mappingmentioning

confidence: 99%

Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Lundquist

Hammarstrand

Gustafsson

2011

IEEE Trans. Signal Process.

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“…Every time instant, an estimate of the disturbanceψ d is avilable over a finite time horizon. Estimates of this disturbance signal can be obtained on-line by using the measurement setups used in [9], [1], [5].…”

Section: A Vehicle Modelmentioning

confidence: 99%

Real-time implementation of a novel safety function for prevention of loss of vehicle control

Ali

Sjöberg

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-We present a novel safety function for prevention of vehicle control loss. The safety function overcomes some of the limitations of conventional Electronic Stability Control (ESC) systems. Based on sensor information about the host vehicle's state and the road ahead, a threat assessment algorithm predicts the future evolution of the vehicle's state. If the vehicle motion, predicted over a finite time horizon violates safety constraints, autonomous deceleration is activated in order to prevent vehicle loss of control. The safety function has been implemented in real-time. Experimental results indicate that the safety function relies less on the driver's skills than conventional ESC systems and that a more controllable and comfortable vehicle motion can be acquired when the function is active.

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“…Assumption 2 implies that the road geometry is known over a future time horizon. The sensing technologies used in, e.g., [9], [12], [4] can be used for this purpose.…”

Section: System Constraintsmentioning

confidence: 99%

Model-based threat assessment in semi-autonomous vehicles with model parameter uncertainties

Ali

Falcone

Sjöberg

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-In this paper, we consider model-based threat assessment methods which rely on vehicle and driver mathematical models and are based on reachability analysis tools and set invariance theory. We focus on the parametric uncertainties of the driver mathematical model and show how these can be accounted for in the threat assessment. The novelty of the proposed methods lies in the inclusion of the driver model uncertainties in the threat assessment problem formulation and in their validation through experimental data. We show how different ways of accounting for the model uncertainties impact the capabilities and the effectiveness of the proposed algorithms in detecting hazardous driving situations.

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Joint road geometry estimation and vehicle tracking

Cited by 42 publications

References 10 publications

Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Real-time implementation of a novel safety function for prevention of loss of vehicle control

Model-based threat assessment in semi-autonomous vehicles with model parameter uncertainties

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