Driving simulators have been used in the automotive industry for many years because of their ability to perform tests in a safe, reproducible and controlled immersive virtual environment. The improved performance of the simulator and its ability to recreate in-vehicle experience for the user is established through motion cueing algorithms (MCA). Such algorithms have constantly been developed with model predictive control (MPC) acting as the main control technique. Currently, available MPC-based methods either compute the optimal controller online or derive an explicit control law offline. These approaches limit the applicability of the MCA for real-time applications due to online computational costs and/or offline memory storage issues. This research presents a solution to deal with issues of offline and online solving through a hybrid approach. For this, an explicit MPC is used to generate a look-up table to provide an initial guess as a warm-start for the implicit MPC-based MCA. From the simulations, it is observed that the presented hybrid approach is able to reduce online computation load by shifting it offline using the explicit controller. Further, the algorithm demonstrates a good tracking performance with a significant reduction of computation time in a complex driving scenario using an emulator environment of a driving simulator.

In the transition from partial to high automation, occupants will no longer be actively involved in driving. This will allow the use of travel time for work or leisure, where high comfort levels preventing motion sickness are required. In this paper, an optimal trajectory planning algorithm is presented in order to minimise motion sickness in automated vehicles. A predefined path is provided as an input to the algorithm, to generate an optimal path with limited lateral deviation and the corresponding optimal velocity profile, for the minimisation of motion sickness. An optimal control problem is formulated with a cost function combining both motion sickness and travel time. For a sickening curvy road, the algorithm reduced the motion sickness dose value (MSDV) up to 52% depending on the allowed lateral deviation and the weighting on travel time. The efficacy of the proposed algorithm has been evaluated via human-in-the-loop experiments using a moving-base driving simulator. Motion cueing parameters were selected to optimally transmit the sickening stimuli resulting in close to full vibration transmission above 0.2 Hz. During the experiment, the participants were asked to rate their experience based on the standard MIsery SCore ratings. According to these, sickness levels were reduced on average by 65% with reduced motion sickness in all 16 participants.

While several synchronization-based protocols have been provided for formation-keeping of cooperative vehicles, the problem of synchronized merging is more challenging. Challenges associated to the merging scenario include the need for establishing bidirectional interaction (in place of unidirectional look-ahead interaction), and the need for considering different engine dynamics (in place of homogeneous engine dynamics). This work shows how such challenges can be tackled via a newly proposed strategy based on adaptive control with bidirectional error: the adaptive control framework autonomously adapts to different engine dynamics, while the bidirectional error seamlessly allows the vehicle that wants to merge to interact with both the front and the rear vehicles, in a similar way as humans do.

Recently proposed adaptive platooning strategies for connected automated vehicles are able to cope with uncertain vehicle parameters (uncertain driveline time constants), but can handle only acyclic graphs like look-ahead graphs. This prevents from enhancing platooning protocols with synchronized merging maneuvers, where cyclic communication is needed and creates algebraic loops that require well posedness of the inputs. We propose an adaptive platooning strategy for synchronized merging in the cyclic communication scenario. The protocol adopts a set of adaptive control laws, designed via Lyapunov stability theory to cope with uncertain driveline time constants. Well-posedness of the inputs is proven in a distributed way (using information from neighboring vehicles) in spite of uncertainty and cyclic communication. The proposed strategy is shown in a benchmark merging scenario.

In adaptive platooning strategies proposed in literature to handle uncertain and nonidentical uncertain vehicle dynamics (uncertain heterogeneous platoons) two aspects requiring proper design are neglected: bidirectional interaction among vehicles which might lead to loss of string stability, and engine saturation constraints which might lead to loss of cohesiveness. This work proposes a novel adaptive platooning strategy handling these two crucial aspects. Specifically, bidirectional interaction is handled by designing bidirectional reference dynamics with proven string stability properties, to which the uncertain heterogeneous platoon should homogenize; engine constraints are handled via a proposed a mechanism that makes such reference dynamics 'not too demanding', by properly saturating their action. The saturation action will allow all vehicles in the platoon to not hit their engine limits, preserving cohesiveness. Simulations are conducted to validate the theoretical analysis and show the effectiveness of the method in retaining cohesiveness of the platoon.