Using road topology to improve cyclist path prediction

Pool, Ewoud A. I.; Kooij, Julian F. P.; Gavrila, Dariu M.

doi:10.1109/ivs.2017.7995734

Cited by 45 publications

(43 citation statements)

References 24 publications

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“…In the IV domain, behavior is typically tied to road infrastructure (Oniga et al 2008;Geiger et al 2014;Kooij et al 2014b;Sattarov et al 2014;Pool et al 2017). Road layout can be obtained from localization using GPS and INS sensors (Schreiber et al 2013) to retrieve information map data on the surrounding infrastructure.…”

Section: Static Environment Cuesmentioning

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: Static Environment Cuesmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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“…Ballan et al [9] learn preferred routes directly on image data rather than the semantic information, and show that the learned knowledge is transferable to new locations. Another approach is to directly encode the structure of the road ahead to limit the possible paths that the VRU can take [2]. Dynamic objects can also influence the future path of VRUs.…”

Section: Previous Workmentioning

confidence: 99%

“…predict where other road users are likely to be in the near future. Contextual information, such as spatial layout [1], [2], [3], or class-specific visual cues (a car blinker, an cyclists' outstretched arm, etc) [4] can be used to improve the accuracy of such predictions compared to using positional information only.…”

Section: Introductionmentioning

confidence: 99%

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Context-based cyclist path prediction using Recurrent Neural Networks

Pool

Kooij

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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This paper proposes a Recurrent Neural Network (RNN) for cyclist path prediction to learn the effect of contextual cues on the behavior directly in an end-to-end approach, removing the need for any annotations. The proposed RNN incorporates three distinct contextual cues: one related to actions of the cyclist, one related to the location of the cyclist on the road, and one related to the interaction between the cyclist and the egovehicle. The RNN predicts a Gaussian distribution over the future position of the cyclist one second into the future with a higher accuracy, compared to a current state-of-the-art model that is based on dynamic mode annotations, where our model attains an average prediction error of 33 cm one second into the future.

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“…There is still fewer research concerning intention detection of cyclists. In [9], Pool et al introduced a motion model for cyclist path prediction from a moving vehicle including knowledge of the local road topology. The authors were able to improve the prediction accuracy by incorporation of different motion models for canonical directions.…”

Section: B Related Workmentioning

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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Using road topology to improve cyclist path prediction

Cited by 45 publications

References 24 publications

Context-Based Path Prediction for Targets with Switching Dynamics

Context-Based Path Prediction for Targets with Switching Dynamics

Context-based cyclist path prediction using Recurrent Neural Networks

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

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