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Found 9 papers, 0 papers with code
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information
This article presents a novel approach to incorporate visual cues from video-data from a wide-angle stereo camera system mounted at an urban intersection into the forecast of cyclist trajectories.
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories
We compare our method with an approach that provides forecasts in the form of Gaussian distributions and discuss the respective advantages and disadvantages.
Cyclist Intention Detection: A Probabilistic Approach
A basic movement detection based on motion history images (MHI) and a residual convolutional neural network (ResNet) are used to estimate probabilities for the current cyclist motion state.
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e. g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties.
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning
Furthermore, the architecture is used to evaluate motion-specific physical models for starting and stopping and video-based pedestrian motion classification.
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
Highly automated driving requires precise models of traffic participants.
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
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.
Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure
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.
Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network
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.
