The project will make traffic safer for all road users. Whilst much attention is given to vehicle traffic safety, other road users such as pedestrians, cyclists, E-scooters and even wheelchair users still remain exposed. Standard transport methods are becoming more autonomous to avoid dangers, but vulnerable users cannot easily be computer-controlled for safety. This project uses augmented awareness to avoid common road risks for vulnerable people. In conjunction with other VRUs, the infrastructure and road monitoring infrastructure communicates directly, or via the cloud, against collision avoidance.
Fig. 1 A systematic view for safer roads.
The beneficiaries are i) vulnerable road users ii) society, which carries much of the cost for rehabilitation after accidents iii) insurance companies who cover the healthcare costs and loss of income iv) new infrastructure businesses v) phone app marketplace. The anonymized data from VRUs and road traffic conditions has potential for both public and commercial entities.
An issue for the business development is to avoid fragmentation of the users. They must be warned irrespective of their operators or devices. The system has to provide full interoperability across business boundaries. Equally important is the privacy of the users to prevent any individualized tracking or personal data disclosure. We will evaluate mobile phones as communicating devices using vehicular protocols such as IEEE 802.11p (G5), LTE Direct (4G) & 5G. Latency is key, see the figure. We will start with V2X and add V-to-VRU, VRU-to-VRU and VRU-to-infrastructure modes. We will develop a cloud-based solution for VRUs. Users will stream data, primarily location and velocity, creating a fast collision-alert database we will use HopsWorks’ Feature Store & Flink or Kafka as ingest mechanisms.
Fig 2. A latency gradient for cycle communications.
Interaction with the user is key, if the system does not provide timely and situation-relevant awareness for its users then it has no value. The system should not distract its’ users, therefore we leverage the previous work done in V2Cyclist. Black spots can be identified using historical data, so routes can be selected such as the safest. In the cloud, automatic analyses using learning algorithms will improve navigation accuracy as well as certain personalisations, such as the audible warnings. Therefore, this project will address this by a combination of smart algorithms and communication technologies that provide VRUs select situated information to prevent them from dangerous situations. The goal is to develop easy-to-use services that can aid in the current traffic situations, and form a basis to be further developed with automated and communicating vehicles on the road.
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