The RELEVANCE project aims to master the challenges of:

1. Enabling a vehicle to predict the reliable communication region in real-time, i.e., the geographical region within which it can reliably exchange traffic and sensor information with other road users.
2. Utilizing reliable communication region prediction for V&V (verification and validation) during the development of ADAS (advanced driver assistant systems) and AD (automated driving).
3. Fast coverage prediction enables the generation and prioritization of the most relevant testing scenarios for V&V. Identified boundaries of coverage allow tests to be efficiently steered towards edge cases which are otherwise hard to find but are highly relevant for safety validation.

Develop HOPE (high-performance open-source computing reference framework) which allows supervised deep learning methods for reliable communication region prediction to be investigated. The framework will consist of modules for channel modelling, FER estimation, sensor data provision (i.e., LiDAR or RADAR data), deep learning models and a simulation which enables vehicle-in-the-loop testing.

The central innovation of the RELEVANCE project is investigating methods that enable the utilization of geometrical database information (streets, buildings, traffic signs and other obstacles) as well as vehicle sensor data to train a deep neural network to predict a vehicle’s reliable communication region in real time.

Start: 01.01.2021

End: 31.12.2022

Results:

The project RELEVANCE will:
(a) investigate the real-time prediction of the reliable wireless communication region of vehicles. Using available processed sensors information from the vehicles’ sensors and a novel dynamic geometry-based stochastic channel model as the input to trained and validated deep neural network predicting the reliable communication range in terms of a FER (frame error rate) estimation.
(b) provide an open dataset on wireless channel measurements enriched with sensor data from a vehicle for coining a scientific challenge on the prediction of the wireless reliable communication region.
(c) develop HOPE (high-performance open-source computing reference framework) which allows to investigate supervised deep learning methods for reliable communication region prediction. The framework will consist of modules for channel modelling, FER estimation, sensor data provision (i.e., LiDAR or RADAR data), deep learning models and a simulation which enables vehicle-in-the-loop testing.

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