Merge recommendations for driver assistance: A cross-modal, cost-sensitive approach

Abstract

In this study, we present novel work focused on assisting the driver during merge maneuvers. We use an automotive testbed instrumented with sensors for monitoring critical regions in the vehicle's surround. Fusing information from multiple sensor modalities, we integrate measurements into a contextually relevant, intuitive, general representation, which we term the Dynamic Probabilistic Drivability Map [DPDM]. We formulate the DPDM for driver assistance as a compact representation of the surround environment, integrating vehicle tracking information, lane information, road geometry, obstacle detection, and ego-vehicle dynamics. Given a robust understanding of the ego-vehicle's dynamics, other vehicles, and the on-road environment, our system recommends merge maneuvers to the driver, formulating the maneuver as a dynamic programming problem over the DPDM, searching for the minimum cost solution for merging. Based on the configuration of the road, lanes, and other vehicles on the road, the system recommends the appropriate acceleration or deceleration for merging into the adjacent lane, specifying when and how to merge.