DeepSense ML Challenges

DeepSense-ITU Multi Modal V2V Beam Tracking Challenge 2023

Vehicle-to-vehicle (V2V) communication is indispensable for intelligent transportation systems (ITS) to enable crucial data exchange among vehicles, enhancing safety, traffic efficiency, and driving experience. Yet, conventional V2V methods struggle with the escalating data complexity, prompting exploration of higher frequencies like millimeter wave (mmWave/sub-THz). However, this shift presents challenges like deploying large antenna arrays and using narrow directive beams to ensure sufficient receive power, resulting in significant beam training overhead. This challenge presents a critical bottleneck for high-mobility, latency-sensitive V2V applications, paving the way for the next V2V frontier: efficient and effective beam prediction/tracking.

Given a multi-modal training dataset comprising sequences of 360-degree RGB images and vehicle positions from different locations with diverse environmental characteristics, the goal is to develop machine learning models capable of accurate sensing-aided V2V beam tracking.