Cooperative starting intention detection of cyclists based on smart devices and infrastructure

Bieshaar, Maarten; Zernetsch, Stefan; Depping, Malte; Sick, Bernhard; Doll, Konrad

doi:10.1109/itsc.2017.8317691

Cited by 22 publications

(18 citation statements)

References 12 publications

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“…In this article we propose a cooperative approach to track cyclists at an urban intersection robustly and accurately. The cooperatively obtained positional information can then subsequently be used for intention detection [2]. In contrast to bare data fusion, cooperation also captures the interactions between different participants.…”

Section: A Motivationmentioning

confidence: 99%

“…In [1], we presented a cooperative, holistic concept to detect intentions of VRUs by means of collective intelligence, including smart devices carried by the VRU itself. We proposed an approach to cooperatively detect cyclists' starting motion and to forecast their future trajectory in [2]. The approach was limited in its application due to the requirement of precise positional information for the trajectory forecast.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: A Motivationmentioning

confidence: 99%

Section: Related Workmentioning

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 this article, we focus on detecting the starting movement. An early basic movement detection can support the trajectory forecast [2].…”

Section: Related Workmentioning

confidence: 99%

“…How this trade-off is solved (i.e., which model parametrization is considered) depends on the rating of the goal. If the starting movement detection is used as supplementary information supporting the trajectory forecast [2], then allowing a few false positive detections might be acceptable while in other cases having zero false positive detections is mandatory.…”

Section: A Detection Of Starting Movementsmentioning

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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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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“…Goldhammer et al [7] and Gao et al [8] combined multi-layer perception (MLP) and polynomial fitting to predict the future trajectory of the rider. Bieshaar et al [9] used MLP to detect the starting intention of cyclists at intersections based on smart devices and infrastructure. In addition, long short-term memory (LSTM) was increasingly used as a time recurrent neural network for trajectory prediction [10,11].…”

Section: Introductionmentioning

confidence: 99%

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

Gao

Cai

et al. 2021

Sci. China Inf. Sci.

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Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists' intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists' trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.

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Cooperative starting intention detection of cyclists based on smart devices and infrastructure

Cited by 22 publications

References 12 publications

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure

Starting Movement Detection of Cyclists Using Smart Devices

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

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