English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play animportant role in future traffic. To avoid accidents and achieve a highlyefficient traffic flow, it is important to detect VRUs and to predict theirintentions. In this article a holistic approach for detecting intentions ofVRUs by cooperative methods is presented. The intention detection consists ofbasic movement primitive prediction, e.g. standing, moving, turning, and aforecast of the future trajectory. Vehicles equipped with sensors, dataprocessing systems and communication abilities, referred to as intelligentvehicles, acquire and maintain a local model of their surrounding trafficenvironment, e.g. crossing cyclists. Heterogeneous, open sets of agents(cooperating and interacting vehicles, infrastructure, e.g. cameras and laserscanners, and VRUs equipped with smart devices and body-worn sensors) exchangeinformation forming a multi-modal sensor system with the goal to reliably androbustly detect VRUs and their intentions under consideration of real timerequirements and uncertainties. The resulting model allows to extend theperceptual horizon of the individual agent beyond their own sensorycapabilities, enabling a longer forecast horizon. Concealments,implausibilities and inconsistencies are resolved by the collectiveintelligence of cooperating agents. Novel techniques of signal processing andmodelling in combination with analytical and learning based approaches ofpattern and activity recognition are used for detection, as well as intentionprediction of VRUs. Cooperation, by means of probabilistic sensor and knowledgefusion, takes place on the level of perception and intention recognition. Basedon the requirements of the cooperative approach for the communication a newstrategy for an ad hoc network is proposed.

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence