Prediction of Drivers' Subjective Evaluation of Vehicle Reaction Under Aerodynamic Excitations.

OBJECTIVE: The objectives are to determine which quantities are important to measure to determine how drivers perceive vehicle stability, and to develop a regression model to predict which induced external disturbances drivers are able to feel. BACKGROUND: Driver experience of a vehicle's dynamic performance is important to auto manufacturers. Test engineers and test drivers perform several on-road assessments to evaluate the vehicle's dynamic performance before sign-off for production. The presence of external disturbances such as aerodynamic forces and moments play a significant role in the overall vehicle assessment. As a result, it is important to understand the relation between the subjective experience of the drivers and these external disturbances acting on the vehicle. METHOD: A sequence of external yaw and roll moment disturbances of varying amplitudes and frequencies is added to a straight-line high-speed stability simulation test in a driving simulator. The tests are performed with both common and professional test drivers, and their evaluations to these external disturbances are recorded. The sampled data from these tests are used to generate the needed regression model. RESULTS: A model is derived for predicting which disturbances drivers can feel. It quantifies difference in sensitivity between driver types and between yaw and roll disturbances. CONCLUSION: The model shows a relationship between steering input and driver sensitivity to external disturbances in a straight-line drive. Drivers are more sensitive to yaw disturbance than roll disturbance and increased steering input lowers sensitivity. APPLICATION: Identify the threshold above which unexpected disturbances such as aerodynamic excitations can potentially create unstable vehicle behaviour.

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. However, the high cost of the battery electric heavy vehicles is a barrier hindering their adoption by the transportation companies. Automated driving systems, requiring no human driver on-board, make the battery electric heavy vehicles competitive to their conventional counterparts in a wider range of transportation tasks and use cases compared to the vehicles with human drivers. The presented data identify transportation tasks where the battery electric heavy vehicles driven by humans or by automated driving systems have lower cost of ownership than their conventional counterparts. The data were produced by optimizing the vehicle propulsion system together with the loading/unloading schemes and charging powers, with the objective of minimizing the total cost of ownership on 3072 different transportation scenarios, according to research article "Impact of automated driving systems on road freight transport and electrified propulsion of heavy vehicles" (Ghandriz et al., 2020) [2]. The data help understanding the effects of traveled distance, road hilliness and vehicle size on the total cost of ownership of the vehicles with different propulsion and driving systems. Data also include sensitivity tests on the uncertain parameters.

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. The method is applied, in a specific use case, by running a simulation of a truck applying automatic braking on a split friction surface. Here the required driver steering angle, to maintain the intended direction, is limited by a constant threshold. This constant is automatically accounted for when balancing actuator usage in the method. RESULTS: Simulation results show that the actual required driver steering angle can be expected to match the set constant well. Furthermore, the stopping distance is very much affected by this set capability of the driver to handle the lateral disturbance, as expected. CONCLUSION: In general the capability of the driver to handle disturbances should be estimated in real-time, considering driver mental state. By using the method it will then be possible to estimate e.g. stopping distance implied from this. The setup has the potential of even shortening the stopping distance, when the driver is estimated as active, this compared to currently available systems. The approach is feasible for real-time applications and requires only measurable vehicle quantities for parameterization. Examples of other suitable applications in scope of the method would be electronic stability control, lateral stability control at launch and optimal cornering arbitration.