Cyclists' starting behavior at intersections

Hubert, Andreas; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard

doi:10.1109/ivs.2017.7995856

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…Similar studies are also being performed for cyclists. Zernetsch et al (2016) collected data at a single intersection for path prediction of a starting cyclists, and Hubert et al (2017) used the same data to find indicators of cyclist starting behavior. Some studies have used naturalistic data to detect and classify critical vehicle-cyclist interactions at intersections (Sayed et al 2013;Vanparijs et al 2015;Cara and de Gelder 2015), while others use simulations to study bicycle motion at intersections Zhang et al 2017).…”

Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target's behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the Intelligent Vehicles domain concerning pedestrians and cyclists, so-called Vulnerable Road Users (VRUs). Here, context cues include the static environment of the VRU, its dynamic environment, and its observed actions. Experiments using stereo vision data from a moving vehicle demonstrate that the proposed approach results in more accurate path prediction than SLDS at the relevant short time horizon (1 s). It slightly outperforms a computationally more demanding state-of-the-art method.

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Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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“…The triangulated head position and velocity and yaw rate estimates are then combined using an extended Kalman filter implementing the cooperative tracking. We focus on tracking the head for two reasons: First, the head is a good indicator for human intentions [11], second, it is in plain view from different camera perspectives and, therefore, perfectly suited for triangulation. Moreover, the integration of smart device based velocity and yaw rate estimates allows to track a cyclist even in the absence of any visual information.…”

Section: Methodsmentioning

confidence: 99%

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure

Reitberger

Bieshaar

Zernetsch

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.

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“…The information can for example be supplied by infrastructure based sensors or vehicles. As shown in [37], these features based on the cyclist's head trajectory are a valuable source of information for predicting cyclist intentions. The head trajectory is used to calculate the magnitude of the velocity and subsequently features based on orthogonal polynomial approximations for window lengths of 0.2 s and 0.8 s are extracted.…”

Section: E Cooperative Movement Primitive Detectionmentioning

confidence: 99%

Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble

Bieshaar

Zernetsch

Hubert

et al. 2018

IEEE Trans. Intell. Veh.

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In future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this article we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing featureand decision level cooperation. We introduce a novel method based on a 3D Convolutional Neural Network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.M. Bieshaar, and B. Sick are with the Intelligent Embedded Systems Lab,

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Cyclists' starting behavior at intersections

Cited by 14 publications

References 8 publications

Context-Based Path Prediction for Targets with Switching Dynamics

Context-Based Path Prediction for Targets with Switching Dynamics

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure

Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble

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