The acceptance of automated driving is under the potential threat of motion sickness. It hinders the passengers' willingness to perform secondary activities. In order to mitigate motion sickness in automated vehicles, we propose an optimization-based motion planning algorithm that minimizes the distribution of acceleration energy within the frequency range that is found to be the most nauseogenic. The algorithm is formulated into integral and receding-horizon variants and compared with a commonly used alternative approach aiming to minimize accelerations in general. The proposed approach can reduce frequency-weighted acceleration by up to 11.3% compared with not considering the frequency sensitivity for the price of reduced overall acceleration comfort. Our simulation studies also reveal a loss of performance by the receding-horizon approach over the integral approach when varying the preview time and nominal sampling time. The computation time of the receding-horizon planner is around or below the real-time threshold when using a longer sampling time but without causing significant performance loss. We also present the results of experiments conducted to measure the performance of human drivers on a public road section that the simulated scenario is actually based on. The proposed method can achieve a 19% improvement in general acceleration comfort or a 32% reduction in squared motion sickness dose value over the best-performing participant. The results demonstrate considerable potential for improving motion comfort and mitigating motion sickness using our approach in automated vehicles.

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This dissertation is dedicated to understanding the potential of improving the motion comfort of automated vehicles and explores multiple options that serve this purpose. Comfort is usually prioritized behind factors such as safety and efficiency but is nevertheless influential to the acceptance of automated vehicles. The goal of enhancing motion comfort overlaps with the need to overcome challenges brought by the motion sickness phenomenon. Motion sickness is found to impact a significant portion of travelers in all types of transport. It tends to develop faster among occupants who are not engaged in the driving task. Its symptoms can cause difficulties for non-driving-related tasks (NDRTs) to be performed effectively by the passengers. Therefore, a part of the research in this dissertation is directed specifically toward mitigating motion sickness in automated vehicles...@en

Automated vehicles promise numerous advantages to their users. The proposed benefits could however be overshadowed by a rise in the susceptibility of passengers to motion sickness due to their engagement in non-driving tasks. Increasing attention is paid to designing vehicle motion to mitigate motion sickness. In this work, the deep reinforcement learning (DRL) method is used to plan vehicle trajectories, with a focus on minimizing low-frequency accelerations. These are known to be the primary cause of motion sickness. The goal is achieved by incorporating a frequency-weighted discomfort term into the reward function during training. The ability of the trained agent to target undesirable frequencies in accelerations is verified by comparing it with another agent trained for improving overall acceleration comfort. A reduction of 9.6% in frequency-weighted discomfort is achieved. The motion plan from the DRL agent is further compared with trajectories generated by human drivers in real-world scenarios. The results demonstrate comparable performance between the DRL agent and human drivers. Meanwhile, a significant reduction in online computation time has been observed when compared to a motion planner based on numerical optimization.

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Motion comfort is the basis of many societal benefits promised by automated driving and motion planning is primarily responsible for this. By planning the spatial trajectory and the velocity profile, motion planners can significantly enhance motion comfort, ideally without sacrificing time efficiency. Active suspensions can push the boundary further by enabling additional degrees of freedom in the controllable vehicle motions. In this paper, we propose to integrate the planning of roll motion into an optimization-based motion planning algorithm called 3DOP(3 Degrees-of-Freedom Optimal Planning), where the conflicting objectives of comfort and time efficiency are optimized. The feasibility of the planned motion is verified in a realistic simulation environment, where feedforward-proportional control suffices to track the speed, path, and roll references. The proposed scheme achieves a significant reduction of motion discomfort, namely by up to 28.1% over the variant without controllable roll motion, or up to 34.2% over an acceleration-bounded driver model. The results suggest considerable potential for improving motion comfort by equipping automated vehicles with active suspensions.@en

The benefits of automated driving can only be fully realized if the occupants are protected from motion sickness. Active suspensions hold the potential to raise the comfort level in automated passenger vehicles by enabling new functionalities in chassis control. One example is to actively lean the vehicle body toward the center of the corner to counteract the inertial lateral acceleration. Commonly known as curve tilting, the concept is deemed effective in reducing postural disturbance on the occupants and the visual-vestibular conflict when the occupants do not have an external view. We present in this article a nonlinear model predictive control (NMPC) method for the curve tilting functionality. The controller incorporates the nonlinear suspension forces in the prediction model to help achieve high tracking accuracy near the physical limit of the suspension system. The optimization process is accelerated with an explicit initialization method that is based on piecewise-affine (PWA) modeling and offline solution to an alternative optimal control problem (OCP). The controller is able to operate at 20 Hz in a hardware-in-the-loop (HIL) setup. Given sufficient computational resources, we observe a significant reduction in the lateral acceleration sensed by the passenger over a vehicle with passive suspensions, namely, by 46.5%, 25.4%, and 25.4% in the highway, rural, and urban driving scenarios, respectively. The NMPC also outperforms the baseline proportional-integral-derivative (PID) controller by achieving lower tracking error, namely, by 12.9%, 16.4%, and 38.0% in the aforementioned scenarios.

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The public acceptance of automated driving is influenced by multiple factors. Apart from safety being of top priority, comfort and time efficiency also have an impact on the popularity of automated vehicles. These two factors contradict each other as optimizing for one results in the degradation of the other. We investigate in this paper how such a multiobjective problem is approached by human drivers and by numerical optimization in the roundabout scenario, which is compact in size but complex to handle. The human drivers' behavior is first observed using naturalistic driving data. The average trajectories and distribution of peak accelerations were extracted after model-based fitting and removal of erroneous samples. The processed data is shared online as an open-access dataset. Then, an optimization problem is formulated and solved to find the numerically optimal motion profile in terms of comfort and time efficiency. The weighted sum of travel time and discomfort is minimized. By adjusting the weight distribution, we present different motion profiles favoring optimal comfort, human-like acceleration magnitudes, and agility, respectively.@en

Many studies have been recently exploited to discuss the path following control algorithms for automated vehicles using various control techniques. However, path following algorithm considering the possibility of automated vehicles with rear wheel steering (RWS) is still less investigated. In this study, we implemented nonlinear model predictive control (NMPC) on a passenger vehicle with active RWS for path following. The controller was compared to two other variations of NMPC where the rear steering angle is proportional to the front or fixed to zero. Simulation results suggested that the proposed controller outperforms the other two variations and the baseline controllers (Stanley and LQR) in terms of accuracy and responsiveness.

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Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP. This is done by combining the MPCA solver with an online adaptive parameter estimation (APE) algorithm. In this study, an advanced solution to the OCAP is proposed by combining MPCA with APE. This solution coordinates differential braking and active front steering (AFS) of a passenger vehicle, to stabilize the lateral and yaw motion. The simulation results indicate that the APE+MPCA combination effectively accounts for actuator dynamics and actuator parameter mismatches.@en

Loss of lateral stability remains a major cause of road accidents in recent years. Further improvement of passenger vehicle's active safety requires a more efficient utilization of the tire-road friction. Nonlinear model predictive control (NMPC) is expected to fulfill such a role, as the nonlinear characteristics of the vehicle are included and the control input is optimized. However, the computational load can be excessive for onboard hardware, which hinders the NMPC from practical implementation. To tackle the problem, this study proposes a method to improve the computational efficiency in NMPC. The proposed solution consists of an explicitly stored look-up table for generating initial guesses and an online optimization component. The look-up table is based on the offline solution of a hybrid MPC controller. Through the simulation with multibody vehicle model, impressive control performance has been observed, as the vehicle can be stabilized from a side-slip angle of up to 0.5 rad.

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This paper presents our research platform SafeVRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasis on the environment perception and planning modules. The environment perception detects the VRUs using a stereo camera and predicts their paths with Dynamic Bayesian Networks (DBNs), which can account for switching dynamics. The motion planner is based on model predictive contouring control (MPCC) and takes into account vehicle dynamics, control objectives (e.g., desired speed), and perceived environment (i.e., the predicted VRU paths with behavioral uncertainties) over a certain time horizon. We present simulation and real-world results to illustrate the ability of our vehicle to plan and execute collision-free trajectories in the presence of VRUs.@en