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

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

doi:10.1109/tiv.2018.2873900

Cited by 21 publications

(16 citation statements)

References 32 publications

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“…Nevertheless, these methods fail in case of bad visibility or occlusion. We see the information delivered by smart devices as complementing the cooperative intention detection process [12], e.g., allowing to resolve occlusion situations.…”

Section: Discussionmentioning

confidence: 99%

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Starting Movement Detection of Cyclists Using Smart Devices

Bieshaar

Depping

Schneegans

et al. 2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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In near future, vulnerable road users (VRUs) such as cyclists and pedestrians will be equipped with smart devices and wearables which are capable to communicate with intelligent vehicles and other traffic participants. Road users are then able to cooperate on different levels, such as in cooperative intention detection for advanced VRU protection. Smart devices can be used to detect intentions, e.g., an occluded cyclist intending to cross the road, to warn vehicles of VRUs, and prevent potential collisions. This article presents a human activity recognition approach to detect the starting movement of cyclists wearing smart devices. We propose a novel two-stage feature selection procedure using a score specialized for robust starting detection reducing the false positive detections and leading to understandable and interpretable features. The detection is modelled as a classification problem and realized by means of a machine learning classifier. We introduce an auxiliary class, that models starting movements and allows to integrate early movement indicators, i.e., body part movements indicating future behaviour. In this way we improve the robustness and reduce the detection time of the classifier. Our empirical studies with real-world data originating from experiments which involve 49 test subjects and consists of 84 starting motions show that we are able to detect the starting movements early. Our approach reaches an F1 F1 F1-score of 67% within 0.33 s after the first movement of the bicycle wheel. Investigations concerning the device wearing location show that for devices worn in the trouser pocket the detector has less false detections and detects starting movements faster on average. We found that we can further improve the results when we train distinct classifiers for different wearing locations. In this case we reach an F1 F1 F1-score of 94% with a mean detection time of 0.34 s for the device worn in the trouser pocket.

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Section: Discussionmentioning

confidence: 99%

“…In our previous work [2], [12], we presented a cooperative approach to cyclists' starting intention detection involving smart devices as an essential component. The approach presented in this article is an extension with special focus solely on smart devices.…”

Section: Related Workmentioning

confidence: 99%

Starting Movement Detection of Cyclists Using Smart Devices

Bieshaar

Depping

Schneegans

et al. 2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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“…Vehicles and other traffic participants are linked in the future and fitted with various sensors, allowing for communication on various levels, such as situation prediction and intention detection. In their cooperative approach for cyclist beginning action identification, Maarten Bieshaar et al [62] use a boosted stacking ensemble system to realize feature and judgment level cooperation. A novel technique based on 3D Convolutional Neural Network (CNN) is proposed to detect beginning motions on image sequences by studying spatial and temporal characteristics.…”

Section: Convolutional Neural Network (Cnn) Modelmentioning

confidence: 99%

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Kolekar

Gite²,

Pradhan

et al. 2021

IEEE Access

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Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques.Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation. INDEX TERMS intelligent driving; deep learning; intelligent vehicles; vehicle behavior prediction; pedestrian behavior prediction. Inclusion criteriaResearch articles must be original research articles instead of review/survey articles. Research articles that are published between2011 and 2021.Research articles that answer at least one research question. Research articles must contain search keywords either in the title, abstract, or full text. The proposed solution in the research article must be evaluated. The solution developed must be aimed to solve problems on behavior prediction of traffic actors. Exclusion criteriaNon-English Research articles Duplicate research articles Research articles with non-availability of full text Research articles that are not relevant to pedestrians and vehicle behavior prediction concerning intelligent vehicle III. RESULTSThis section summarizes the findings of systematic analysis. It answers the research questions posed above based on the results of this review process.

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“…In this experiment, we use the VRU trajectory dataset [16][17] [18]. The dataset is recorded by cameras and LiDAR [19] at urban intersections.…”

Section: Experiments 41 Data Preparationmentioning

confidence: 99%

DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection

Lv¹,

Li²,

Dong³

et al. 2021

KSII TIIS

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Compared with vehicle trajectories, pedestrian trajectories have stronger degrees of freedom and complexity, which poses a higher challenge to trajectory prediction tasks. This paper designs a mode to divide the trajectory of pedestrians at a traffic intersection, which converts the trajectory regression problem into a trajectory classification problem. This paper builds a deep model for pedestrian trajectory prediction at intersections for the task of pedestrian shortterm trajectory prediction. The model calculates the spatial correlation and temporal dependence of the trajectory. More importantly, it captures the interactive features among pedestrians through the Attention mechanism. In order to improve the training speed, the model is composed of pure convolutional networks. This design overcomes the single-step calculation mode of the traditional recurrent neural network. The experiment uses Vulnerable Road Users trajectory dataset for related modeling and evaluation work. Compared with the existing models of pedestrian trajectory prediction, the model proposed in this paper has advantages in terms of evaluation indicators, training speed and the number of model parameters.

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

Cited by 21 publications

References 32 publications

Starting Movement Detection of Cyclists Using Smart Devices

Starting Movement Detection of Cyclists Using Smart Devices

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection

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