Applying quantum approximate optimization to the heterogeneous vehicle routing problem.

Quantum computing offers new heuristics for combinatorial problems. With small- and intermediate-scale quantum devices becoming available, it is possible to implement and test these heuristics on small-size problems. A candidate for such combinatorial problems is the heterogeneous vehicle routing problem (HVRP): the problem of finding the optimal set of routes, given a heterogeneous fleet of vehicles with varying loading capacities, to deliver goods to a given set of customers.

Optimization data on total cost of ownership for conventional and battery electric heavy vehicles driven by humans and by automated driving systems.

In road freight transport, the emerging technologies such as automated driving systems improve the mobility, productivity and fuel efficiency. However, the improved efficiency is not enough to meet environmental goals due to growing demands of transportation. Combining automated driving systems and electrified propulsion can substantially improve the road freight transport efficiency.

Combining coordination of motion actuators with driver steering interaction.

Objective: A new method is suggested for coordination of vehicle motion actuators; where driver feedback and capabilities become natural elements in the prioritization.

Methods: The method is using a weighted least squares control allocation formulation, where driver characteristics can be added as virtual force constraints. The approach is in particular suitable for heavy commercial vehicles that in general are over actuated.