Abstract
Keywords - Communications, Wireless, Driver Assistance, Active Safety, V2VVehicle-to-Vehicle communication (V2V) introduces a new mechanism for vehicles to share information with one another. Given the relatively low-latency communication compared to other devices such as cell phones, the information shared via V2V can be used to support use cases which are expected to enhance safety. This essentially introduces a new "sensor" into the vehicle that allows one vehicle to "detect" other equipped vehicles within some distance. However, once vehicles can communicate with each other, what information should they share, and how should they process all of it? Past work in various public projects and standards organizations has identified what information should be shared via periodic messaging. This paper presents a new method for processing the information received and feeding intermediate results to algorithms supporting various use cases.
The calculation of the relative position and location of other vehicles is common for many V2V use cases. Therefore, we propose a new functional block named "Situational Awareness". This block preprocesses the incoming periodic or event-driven messages to identify the location of a vehicle. The Situational Awareness algorithm then offers the data to the use cases which do their specific calculations.
The categorization of the other vehicles by Situational Awareness is based on the GNSS data sent from the other vehicles. Situational Awareness predicts the forward paths of the vehicles and uses the previous driving histories, or trails, to classify other vehicles. With good availability of GNSS data, lane level classification accuracy is achievable.
Additionally, future scenarios will require processing of many OVs in very short time. As a first step towards this goal, a method for fast elimination of non-interesting OVs is presented. The method consists of each use case algorithm communicating its real-time requirements to Situational Awareness. With those requirements, Situational Awareness can make quick decisions about which messages to process and which to ignore.
Real life performance data shows that the proposed Situational Awareness is effective at offering lane level accurate data to the use case algorithms.