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.Comment: 8 pages, 10 figures, 3 tables, conference (DSC 2023

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.Comment: Accepted to the 28th IAVSD International Symposium on Dynamics of Vehicles on Roads and Track

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.Comment: Preprint accepted to 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). A supplementary video is available at https://youtu.be/D5XJ5ncSuq

Vehicle dynamics with brake hysteresis

This paper studies hysteresis of vehicle brakes and its influence on the vehicle dynamics. The experimental investigation clearly shows the non-linear and asymmetric characteristics of hysteresis of the disk brakes in passenger cars. A computational model of the brake mechanism with hysteretic elements, based on the Bouc–Wen method, is developed and verified with experimental data. Using the developed model, the influence of hysteresis on the vehicle dynamics during straight-line braking with an anti-lock braking system is analysed. It is also observed that the variations in the hysteresis parameters have important influences on the main vehicle brake characteristics such as the stopping (brake) distance, the time of braking and the average deceleration. A comparative analysis of the simulation results is also given for braking with zero hysteresis or with hysteresis represented as a signal delay and linear function

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/JenvBComment: Accepted to the journal Engineering Applications of Artificial Intelligence; 19 pages, 18 figures, code: https://git.io/JenvB. arXiv admin note: text overlap with arXiv:1907.0782

Feasibility of a neural network-based virtual sensor for vehicle unsprung mass relative velocity estimation

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity

Vibration-Induced Discomfort in Vehicles: A Comparative Evaluation Approach for Enhancing Comfort and Ride Quality

This article introduces a methodology for conducting comparative evaluations of vibration-induced discomfort. The aim is to outline a procedure specifically focused on assessing and comparing the discomfort caused by vibrations. The article emphasizes the metrics that can effectively quantify vibration-induced discomfort and provides insights on utilizing available information to facilitate the assessment of differences observed during the comparisons. The study also addresses the selection of appropriate target scenarios and test environments within the context of the comparative evaluation procedure. A practical case study is presented, highlighting the comparison of wheel corner concepts in the development of new vehicle architectures. Currently, the evaluation criteria and difference thresholds available allow for comparative evaluations within a limited range of vehicle vibration characteristics

Vibration-induced discomfort in vehicles : a comparative evaluation approach for enhancing comfort and ride quality

This article introduces a methodology for conducting comparative evaluations of vibration-induced discomfort. The aim is to outline a procedure specifically focused on assessing and comparing the discomfort caused by vibrations. The article emphasizes the metrics that can effectively quantify vibration-induced discomfort and provides insights on utilizing available information to facilitate the assessment of differences observed during the comparisons. The study also addresses the selection of appropriate target scenarios and test environments within the context of the comparative evaluation procedure. A practical case study is presented, highlighting the comparison of wheel corner concepts in the development of new vehicle architectures. Currently, the evaluation criteria and difference thresholds available allow for comparative evaluations within a limited range of vehicle vibration characteristics.The European Union Horizon 2020 Framework Program, Marie Skłodowska-Curie actions.https://saemobilus.sae.org/content/V126-10EJhj2024Mechanical and Aeronautical EngineeringSDG-09: Industry, innovation and infrastructur

Coordinated control of multi-actuated electric vehicle

Modern vehicles have the tendency to embed multiple actuators operating jointly to control the motion stability, handling, energy consumption and other operation characteristics. In the thesis, a new solution is provided to the integrated vehicle dynamics control with the prioritization of several vehicle subsystems. The multi-actuated vehicle configuration includes (i) friction brake system, (ii) individual-wheel electric powertrain, (iii) wheel steer actuators, (iv) camber angle actuators, (v) dynamic tire pressure system and (vi) actuators generating additional normal forces. The novel algorithms of subsystem prioritization were proposed based on restriction weights in control allocation. These algorithms achieve lower energy consumption and energy losses without significant impairment to motion stability and vehicle handling as compared to conventional control allocation. The proposed control system has been successfully validated using a hardware-in-the-loop test rig with hardware components of friction brake system and dynamic tire pressure system.DOCTOR OF PHILOSOPHY (EEE

Coordination of steer angles, tyre inflation pressure, brake and drive torques for vehicle dynamics control

During vehicle operation, the control objectives of stability, handling, energy consumption and comfort have different priorities, which are determined by road conditions and driver behavior. To achieve better operation characteristics of vehicle, coordinated control of vehicle subsystems is actively used. The fact of more active vehicle subsystems in a modern passenger car provides more flexibility for vehicle control and control algorithm development. Since the modern vehicle can be considered as over-actuated system, control allocation is an effective control technique to solve such kind of problem. This paper describes coordination of frictional brake system, individual-wheel drive electric motors, active front and rear steering, active camber mechanisms and tyre pressure control system. To coordinate vehicle subsystems, optimization-based control allocation with dynamic weights is applied. The influence of different weights (subsystem restriction) on criteria of vehicle dynamics (RMSE of yaw rate, sideslip angle, dynamic tyre load factor) and energy consumption and losses (consumed/recuperated energy during maneuver, longitudinal velocity decline, tyre energy dissipation) were analyzed. Based on this analysis, the optimal solution was selected. The proposed control strategy is based on the switching between optimal criteria related to vehicle safety and energy efficiency during vehicle motion. Dynamic weights were utilized to achieve this switching. The simulation-based analysis and evaluation of both variants was carried out using a nonlinear vehicle model with detailed models of actuators. The test maneuver is ‘Sine with Dwell’. Both variants of control allocation guarantees vehicle stability of motion and good handling. Meanwhile, proposed variant demonstrates slightly higher longitudinal velocity at the end of maneuver and higher amount of recuperated energy up to 15%; however, tyre dissipation energy increased to 5% compared to optimal solution from simulation-based analysis.Published versio