Stationary Detection of the Pedestrian?s Intention at Intersections

Koehler, Sebastian; Goldhammer, Michael; Bauer, Sebastian; Zecha, Stephan; Doll, Konrad; Brunsmann, U.; Dietmayer, Klaus

doi:10.1109/mits.2013.2276939

Cited by 80 publications

(37 citation statements)

References 37 publications

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“…Nonetheless, there is a yawning gap between gait recognition and pedestrians' intention or action classification. In [13], early indicators of the pedestrian's intention to cross the street are divided into those presumably followed by crossing, e.g. turning the head or catching the vehicle driver's eye, and those definitely followed by entering the lane, e.g.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Pedestrian path prediction using body language traits

Quintero

Almeida

Llorca

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Driver Assistance Systems have achieved a high level of maturity in the latest years. As an example of that, sophisticated pedestrian protection systems are already available in a number of commercial vehicles from several OEMs. However, accurate pedestrian path prediction is needed in order to go a step further in terms of safety and reliability, since it can make the difference between effective and non-effective intervention. In this paper, we consider the three-dimensional pedestrian body language in order to perform path prediction in a probabilistic framework. For this purpose, the different body parts and joints are detected using stereo vision. We propose the use of GPDM (Gaussian Process Dynamical Models) for reducing the high dimensionality of the input feature vector (composed by joints and displacement vectors) in the 3D pose space and for learning the pedestrian dynamics in a latent space. Experimental results show that accurate path prediction can be achieved at a time horizon of ≈ 0.8 s.

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Section: Introduction and Related Workmentioning

confidence: 99%

Pedestrian path prediction using body language traits

Quintero

Almeida

Llorca

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…In addition, trajectory forecasting of VRUs benefits from an early and reliable detection of starting motions, as shown in [3]. While pedestrian movement detection has been analyzed before, e.g., in [4] and [5], cyclists have gained less attention. The proposed system is dedicated to detect cyclist starting motions using infrastructure based sensors which can be part of future intelligent network traffic systems.…”

Section: A Motivationmentioning

confidence: 99%

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

Zernetsch

Kress

Sick

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F 1 -score of 97.7%. The ResNet approach achieved an F 1score of 100% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time.S. Zernetsch, V. Kress and K. Doll are with the Faculty of Engineering, University of Applied Sciences Aschaffenburg,

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“…Thus, only the current position, speed, heading, and acceleration of pedestrians in line-of-sight (in certain cases also in situations without line-of-sight) and within the detection range of on-board sensors can be used for the prediction. Advanced systems-for example, those introduced in Köhler et al (2013)-additionally use the potential of head and foot positions for the prediction. New information sources allow new prediction models to use additional information about pedestrians that is not directly measurable by on-board sensors of the vehicle.…”

Section: State Of the Artmentioning

confidence: 99%

“…In particular, the evaluation of head position (Kloeden et al 2014) and foot movement (Köhler et al 2013) shows high potential for this task.…”

Section: State Of the Artmentioning

confidence: 99%

Concept of an enhanced V2X pedestrian collision avoidance system with a cost function–based pedestrian model

Kotte

Schmeichel

Zlocki

et al. 2017

Traffic Injury Prevention

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Objective: State-of-the-art collision avoidance and collision mitigation systems predict the behavior of pedestrians based on trivial models that assume a constant acceleration or velocity. New sources of sensor information-for example, smart devices such as smartphones, tablets, smartwatches, etc.-can support enhanced pedestrian behavior models. The objective of this article is the development and implementation of a V2Xpedestrian collision avoidance system that uses new information sources. Methods: A literature review of existing state-of-the-art pedestrian collision avoidance systems, pedestrian behavior models in advanced driver assistance systems (ADAS), and traffic simulations is conducted together with an analysis of existing studies on typical pedestrian patterns in traffic. Based on this analysis, possible parameters for predicting pedestrian behavior were investigated. The results led to new requirements from which a concept was developed and implemented.Results: The analysis of typical pedestrian behavior patterns in traffic situations showed the complexity of predicting pedestrian behavior. Requirements for an improved behavior prediction were derived. A concept for a V2X collision avoidance system, based on a cost function that predicts pedestrian near future presence, and its implementation is presented. The concept presented considers several challenges such as information privacy, inaccuracies of the localization, and inaccuracies of the prediction. Conclusion:A concept for an enhanced V2X pedestrian collision avoidance system was developed and introduced. The concept uses new information sources such as smart devices to improve the prediction of the pedestrian's presence in the near future and considers challenges that come along with the usage of these information sources.

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Stationary Detection of the Pedestrian?s Intention at Intersections

Cited by 80 publications

References 37 publications

Pedestrian path prediction using body language traits

Pedestrian path prediction using body language traits

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

Concept of an enhanced V2X pedestrian collision avoidance system with a cost function–based pedestrian model

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