Multiple hypothesis tracking for automated vehicle perception

The use of multiple hypothesis tracking has proven to provide significant performance benefits over the single hypothesis GNN or the PDA algorithm. Automotive sensors like radars, laser-scanners or vision systems are being integrated into vehicles for commercial or scientific purposes, in increasing numbers over the last years. As a result, there is profound literature on this area and several approaches have been proposed to the problem of multi-target, multi-sensor target tracking. The most advanced vehicle applications allow the use of highly or even fully automated driving. Of course, these applications require an accurate, robust and reliable perception output so that the vehicle can be driven autonomously. The HAVEit EU project investigates the application and validation of automated vehicles applications, technologies that are going to have great impact in transport safety and comfort. In this paper the MHT algorithm is applied to real sensor data, installed in Volvo Technology vehicle demonstrating Automated Queue Assistance. In conjunction with simulated scenarios, the benefits in tracking performance compared to conventional GNN tracking are presented.