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Multi-sensor selection optimization and driver warning decision for dynamical virtual driving simulator

Abstract This article presents a framework for the design and development of a multi-sensor selection optimization mechanism for a driver assistance simulation system. The multi-sensor selection mechanism is formulated as an optimization problem and solved by integer linear programming. Both static and dynamic driving scenarios are considered in the formulation; where a static scenario denotes an off-line multi-sensor selection problem and a dynamic scenario denotes an online driving condition transformation problem. The objective function for determining an optimal set of sensors is in terms of guaranteed coverage, energy, bandwidth, and reliability of sensors. For different driving conditions, and driver commands, a selection algorithm is developed for identifying the environment around the vehicle and warning the driver. The selection algorithm and multiple-vehicle driving examples are successfully implemented in a virtual driving simulator.