Multi-Criteria Evaluation for Sorting Motion Planner Alternatives

Abstract

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.

Integrated Active Seat Suspension for Enhancing Motion Comfort

“…In ND, the improvement of the overall driver feel assessment (SA01), leads to the deterioration of occupants' ride comfort. This conflicting relation is widely discussed in the literature [24], but not often captured. In conventional vehicles, the driver receives cues/feedback from the vehicle that it responds intuitively to the driver's commands (steering, throttle and brake inputs).…”

Sensor Fusion and Advanced Controller for Connected and Automated Vehicles

“…In the decision-making and path-planning layers, a decision-making framework based on the Extended Collision Warning System (ECWS) [ 6 ] and a collision relationship-based decision-making method based on the Deep Recurrent Q Network (CR-DRQN) [ 7 ] were proposed. Furthermore, a path planning method based on the Takagi–Sugeno (TS) fuzzy-model-based approach [ 8 ] and a motion planning method by minimizing motion sickness [ 9 ] were proposed. Some other studies focus on the vehicle control layer for electric vehicles, including a driving-adapt optimization strategy for a Magneto Rheological Fluid Transmission (MRFT) [ 10 ] and a robust speed tracking control strategy for an Integrated Motor-Transmission (IMT) powertrain system [ 11 ].…”

“…The application of motion planning in order to minimize motion sickness in automated vehicles was presented in [ 9 ]. The aim of the path planning solution is to ensure the optimum compromise between motion comfort, safety, driving behavior, energy efficiency, journey time, and riding confidence.…”