Motion Cueing Algorithm for Effective Motion Perception: A frequency-splitting MPC Approach

Model predictive control (MPC) is a promising technique for motion cueing in driving simulators, but its high computation time limits widespread real-time application. This paper proposes a hybrid algorithm that combines filter-based and MPC-based techniques to improve specific force tracking while reducing computation time. The proposed algorithm divides the reference acceleration into low-frequency and high-frequency components. The high-frequency component serves as a reference for translational motion to avoid workspace limit violations, while the low-frequency component is for tilt coordination. The total acceleration serves as a reference for combined specific force with the highest priority to enable compensation of deviations from its reference values. The algorithm uses constraints in the MPC formulation to account for workspace limits and workspace management is applied. The investigated scenarios were a step signal, a multi-sine wave and a recorded real-drive slalom maneuver. Based on the conducted simulations, the algorithm produces approximately 15% smaller root means squared error (RMSE) for the step signal compared to the state-of-the-art. Around 16% improvement is observed when the real-drive scenario is used as the simulation scenario, and for the multi-sine wave, 90% improvement is observed. At higher prediction horizons the algorithm matches the performance of a state-of-the-art MPC-based motion cueing algorithm. Finally, for all prediction horizons, the frequency-splitting algorithm produced faster results. The pre-generated references reduce the required prediction horizon and computational complexity while improving tracking performance. Hence, the proposed frequency-splitting algorithm outperforms state-of-the-art MPC-based algorithm and offers promise for real-time application in driving simulators.

Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling

This paper proposes a non-linear Model Predictive Contouring Control (MPCC) for obstacle avoidance in automated vehicles driven at the limit of handling. The proposed controller integrates motion planning, path tracking and vehicle stability objectives, prioritising obstacle avoidance in emergencies. The controller's prediction model is a non-linear single-track vehicle model with the Fiala tyre to capture the vehicle's non-linear behaviour. The MPCC computes the optimal steering angle and brake torques to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Furthermore, the MPCC is extended with the tyre friction circle to fully exploit the vehicle's manoeuvrability and stability. The MPCC controller is tested using real-time rapid prototyping hardware to prove its real-time capability. The performance is compared with a state-of-the-art Model Predictive Control (MPC) in a high-fidelity simulation environment. The double lane change scenario results demonstrate a significant improvement in successfully avoiding obstacles and maintaining vehicle stability.

Computationally-efficient Motion Cueing Algorithm via Model Predictive Control

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.

Comfort-oriented driving: performance comparison between human drivers and motion planners

Motion planning is a fundamental component in automated vehicles. It influences the comfort and time efficiency of the ride. Despite a vast collection of studies working towards improving motion comfort in self-driving cars, little attention has been paid to the performance of human drivers as a baseline. In this paper, we present an experimental study conducted on a public road using an instrumented vehicle to investigate how human drivers balance comfort and time efficiency. The human driving data is compared with two optimization-based motion planners that we developed in the past. In situations when there is no difference in travel times, human drivers incurred an average of 23.5% more energy in the longitudinal and lateral acceleration signals than the motion planner that minimizes accelerations. In terms of frequency-weighted acceleration energy, an indicator correlated with the incidence of motion sickness, the average performance deficiency rises to 70.2%. Frequency-domain analysis reveals that human drivers exhibit more longitudinal oscillations in the frequency range of 0.2-1 Hz and more lateral oscillations in the frequency range of up to 0.2 Hz. This is reflected in time-domain data features such as less smooth speed profiles and higher velocities for long turns. The performance difference also partly results from several practical matters and additional factors considered by human drivers when planning and controlling vehicle motion. The driving data collected in this study provides a performance baseline for motion planning algorithms to compare with and can be further exploited to deepen the understanding of human drivers.

Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles

To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and employ drifting on some segments. These rules are suboptimal and do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the question "When to go into which mode and how to drive in it?" remains unanswered. To choose the suitable mode (discrete decision), the algorithm needs information about the feasibility of the continuous motion in that mode. This makes it a class of Task and Motion Planning (TAMP) problems, which are known to be hard to solve optimally in real-time. In the AI planning community, search methods are commonly used. However, they cannot be directly applied to TAMP problems due to the continuous component. Here, we present a search-based method that effectively solves this problem and efficiently searches in a highly dimensional state space with nonlinear and unstable dynamics. The space of the possible trajectories is explored by sampling different combinations of motion primitives guided by the search. Our approach allows to use multiple locally approximated models to generate motion primitives (e.g., learned models of drifting) and effectively simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left). Our code is available at https://git.io/JenvB

Effects of seat back height and posture on 3D vibration transmission to pelvis, trunk and head

Vibration transmission is essential in the design of comfortable vehicle seats but knowledge is lacking on 3D trunk and head motion and the role of seat back and posture. We hypothesized that head motion is reduced when participants upper back is unsupported, as this stimulates active postural control. We developed an experimental methodology to evaluate 3D vibration transmission from compliant seats to the human body. Wide-band (0.1-12 Hz) motion stimuli were applied in fore-aft, lateral and vertical direction to evaluate the translational and rotational body response in pelvis, trunk and head. A standard car seat was equipped with a configurable and compliant back support to test 3 support heights and 3 sitting postures (erect, slouched, and preferred) where we also tested head down looking at a smartphone. Seat back support height and sitting posture substantially affected vibration transmission and affected low-frequency responses in particular for body segment rotation. According to our hypothesis a low support height proved beneficial in reducing head motion. Relevance to industry: Our methodology effectively evaluates 3D wide-band vibration transmission from compliant seats to the human body. The lowest back support height reduced head motion but was perceived as least comfortable. This calls for seat designs which support but do not so much constrain the upper back. The head down posture enlarged head motion, pleading for computer system integration allowing heads up postures in future automated cars. The biomechanical data will serve to validate human models supporting the design of comfortable (automated) vehicles.

Search-based optimal motion planning for automated driving

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.