Bicycle Model Research Articles

EVALUATION OF PARAMETERS IN SOFTWARE IMPLEMENTATION BAR GRAPH DISPLAY DEVICES

The work is devoted to the estimation of resource consumption of the microcontroller for the synthesis of bar graph discrete-analog data display on the LED information field in bicyclic mode. The paper compares the programs of multicycle information output and its bicyclic analogue for discrete-analog means for the information display systems. The significance of the influence of bicyclic information models on the minimization of machine time resources of a single-chip microcontroller is shown. It is determined that in order to reduce resource consumption in the developed solutions it is necessary to focus on the maximum possible optimization of program blocks that are executed during interrupts and serve the I / O subsystem of the microcontroller. In this sense the bar graph displays based on the bicyclic additive information model has the best results. It was found that the traditional approach to assessing the effectiveness of programs using special benchmark programs, with subsequent measurement of code and execution time of the entire program does not allow to correctly assess the effectiveness of the program and the work of microcontroller at the device design stage. Therefore, as an alternative, it was proposed to use the size of the bytecode of the program and the speed of the main cycle - the procedure of information output to assess the effectiveness of the program. It was found that in terms of speed of execution and consumption of resources, the multicycle version significantly loses to the bicyclic program. Also, reducing the number of image formation cycles in the information field is one of the most effective way to minimize the consumption of microcontroller resources for display services.

Erratum to “Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model”

In the above article <xref ref-type="bibr" rid="ref1">[1]</xref>, Equation (7) was missing a variable. The full corrected equation is as follows:<disp-formula> <tex-math notation="LaTeX">\begin{equation*}\dot{\mathbf{x}}=\lambda f_{\mathrm{dyn}}(\mathbf{x}, \mathbf{u})+(1-\lambda) f_{\operatorname{kin}}(\mathbf{x}, \mathbf{u})=f(\mathbf{x}, \mathbf{u}).\end{equation*} </tex-math></disp-formula>

H2/H∞ robust lateral control of an off-road two-steering-axle vehicle on slippery sloping soils

This paper presents an efficient design for a lateral control synthesis of an off-road vehicle. The considered vehicles have two steering axles and are intended to move on a slippery soil with important lateral and longitudinal slopes. The proposed design relies on an extended bicycle model that accounts for the slopes. The lateral control is designed based on a feed forward/feedback architecture. The feed forward takes advantage of the knowledge of the path characteristics (curvature and slopes), and the feedback ensures robust reference-trajectory tracking. Through an H2/H∞ multi-objective synthesis, the robustness of the lateral controller is ensured with regard to the model uncertainties, path, and soil features. This is important because off-road vehicles’ dynamics are by nature highly variable. To cope with the difficulty caused by uncertain knowledge of key parameters, the proposed robust approach is a practical compromise that maximizes performance under constraints of robustness. The results obtained on a realistic non-linear simulator support this assertion.

Time-Optimal Nonlinear Model Predictive Control for Radar-based Automated Parking

Motion planning and control are crucial components for automated vehicles. Especially in parking scenarios, high precision is required due to the small distances to obstacles. Common approaches utilize ultrasonic or camera sensors. This paper presents a radar-based system architecture for automated parking, which can operate more robustly under difficult weather conditions. Moreover, this work develops a unified planning and control framework based on nonlinear model predictive control. Real vehicles often exhibit dynamic behavior that the commonly used kinematic bicycle model does not represent. Therefore, the paper at hand proposes an extended model, which rests upon a systematic analysis of the test vehicle's dynamics for the low-velocity range. Experiments in simulation and on real-world data show the efficiency of the approach and the importance of the extended vehicle dynamics modeling for closed-loop control.

An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

Modular architecture is very conducive to the development, maintenance, and upgrading of electromechanical products. In the initial stage of module division, the design structure matrix (DSM) is a crucial measure to concisely express the component relationship of electromechanical products through the visual symmetrical structure. However, product structure modeling, as a very important activity, was mostly carried out manually by engineers relying on experience in previous studies, which was inefficient and difficult to ensure the consistency of the model. To overcome these problems, an integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm (GA) is presented. First, the product information extraction algorithm is designed based on the automatic programming structure provided by commercial CAD software, to obtain the assembly, degrees of freedom, and material information needed for modeling. Secondly, based on the evaluation criteria of product component correlation strength, the structural correlation DSM and material correlation DSM of components are established, respectively, and the comprehensive correlation DSM of products is obtained through weighting processing. Finally, the improved GA and the modularity evaluation index Q are used to complete the product module division and obtain the optimal modular granularity. Based on a model in published literature and a bicycle model, comparative studies are carried out to verify the effectiveness and practicality of the proposed method.

Movement Patterns of Electric Cargo Bike Commuters – First Insights from Field Experiments and Trajectory Analyses

Abstract. In addition to and parallel to the SERVUS project, the requirements for a new, agile, urban cargo bicycle with electric drive are identified through a targeted, scientific application potential analysis in the project "E-Cargo Bike - Accompanying Research" described here. These findings are then implemented in the SERVUS project in an agile development and production process. Several prototypes are built in the process. The new e-cargo bicycles should be able to be used both privately and commercially. They will be presented to users for the first time at events (IAA 2021) and rated by them. In addition, the longer-term allocation of the bikes to selected users enables a cross-comparison with existing e-cargo bicycle models. Finally, the substitution potential of journeys in motorized individual transport is estimated and projected using the example of Munich. A key for gathering further information on the abilities of specific electric cargo bike types is tracking experiments with various sensor setups of which the first attempts are presented in this research. Additionally, and by enabling a certain spatial accuracy of gathered GNSS data, we are able to match selected maneuvers of these vehicle types to selected types of the Munich transportation infrastructure. Besides trajectory shapes of maneuvers, we are able to incorporate travel time and delay variations depending on investigation areas and times of the day at selected urban locations for every inspected maneuver (at for examples differing intersection geometries with and without traffic light signals). Another aspect is the presence of bicycle infrastructure and its relation to the option of using mixed traffic modes.

Rollover Prevention Control for Autonomous Electric Road Sweeper

This paper presents a method to prevent the rollover of autonomous electric road sweepers (AERS). AERS have an articulated frame steering (AFS) mechanism. Moreover, the heights of the center of gravity of the front and rear bodies are high. As such, they are prone to rolling over at low speeds and at small articulation angles. A bicycle model with a nonlinear tire model was used as a vehicle model for AERS. Using that vehicle model, path tracking and speed controllers were designed in order to follow a predefined path and speed profile, respectively. To check the rollover propensity of AERS, load transfer ratio (LTR) based the rollover analysis was completed. Based on the results of the analysis, a rollover prevention scheme was proposed. To validate the proposed scheme, a simulation was conducted using a U-shaped path under constant speed conditions. From the simulation, it was shown that the proposed scheme is effective in preventing AERS from rolling over.

Terrain Adaptive Trajectory Planning and Tracking on Deformable Terrains

In this work, a novel single-level adaptive trajectory planner and tracking controller is developed for off-road autonomous vehicles operating on deformable terrains. Trajectory planning and tracking algorithms often rely on a simplified vehicle model to predict future vehicle states based upon control inputs, hence requiring accurate modeling and parameterization. On off-road deformable terrains this is a challenging task due to unknown terrain parameters and the complex interactions at tire-terrain interfaces, which pose issues in continuous differentiability, operating conditions, and computational time. To address these difficulties, in this paper, a neural network deformable terrain terramechanics model and its implementation within a terrain adaptive model predictive control algorithm is presented to improve vehicle safety and performance through more accurate prediction of the plant response. It is shown in simulations that the neural network is able to predict the lateral tire forces accurately and efficiently compared to the Soil Contact Model as a state-of-the-art model and is able to yield accurate bicycle model predictions. It is demonstrated that the implementation of the neural network within model predictive control can outperform both a baseline Pacejka-based and a rapidly exploring random tree controller by improving performance and allowing for more severe maneuvers to be completed that otherwise lead to failure when terrain deformations are not explicitly taken into account. The improved performance achieved through estimating terrain parameters online in an adaptive controller is highlighted against the nonadaptive realization. Finally, it is shown the algorithm is conducive to real-time implementation.

Vehicle dynamics and tire models: An overview

Stability control system plays a significant role in vehicle dynamics to improve the vehicle handling and achieve better stability performance. In order to study and evaluate the performance of the vehicles in addition to how to control it, it is necessary to identify obtain some models related to the dynamics of the vehicle as well as the tire models. This paper presents fundamentals of vehicle dynamics by introducing vehicle models and tire model, which have been widely adopted for vehicle motion control. This helps to get a basic idea of what parameters and states of a vehicle are important in vehicle motion control. This work is separated into four sections: vehicle planar model, full vehicle model, two degrees of freedom vehicle model (bicycle model) to design the controller, and wheel dynamic model.

Lateral stability regulation of intelligent electric vehicle based on model predictive control

Purpose This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm. Design/methodology/approach Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range. Findings The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions. Originality/value The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

A Predictive Safety Filter for Learning-Based Racing Control

The growing need for high-performance controllers in safety-critical applications like autonomous driving motivated the development of formal safety verification techniques. In this letter, we design and implement a predictive safety filter that is able to maintain vehicle safety with respect to track boundaries when paired alongside any potentially unsafe control signal, such as those found in learning-based methods. A model predictive control (MPC) framework is used to create a minimally invasive algorithm that certifies whether a desired control input is safe and can be applied to the vehicle, or that provides an alternate input to keep the vehicle in bounds. To this end, we provide a principled procedure to compute a safe and invariant set for nonlinear dynamic bicycle models using efficient convex approximation techniques. To fully support an aggressive racing performance without conservative safety interventions, the safe set is extended in real-time through predictive control backup trajectories. Applications for assisted manual driving and deep imitation learning on a miniature remote-controlled vehicle demonstrate the safety filter's ability to ensure vehicle safety during aggressive maneuvers.

BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks

Abstract In this paper, we present “BIKED,” a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: 1) How can we explore, understand, and visualize the current design space of bicycles and utilize this information? We apply unsupervised embedding methods to study the design space and identify key takeaways from this analysis. 2) When designing bikes using algorithms, under what conditions can machines understand the design of a given bike? We train a multitude of classifiers to understand designs, then examine the behavior of these classifiers through confusion matrices and permutation-based interpretability analysis. 3) Can machines learn to synthesize new bicycle designs by studying existing ones? We test Variational Autoencoders on random generation, interpolation, and extrapolation tasks after training on BIKED data. The dataset and code are available at http://decode.mit.edu/projects/biked/

P-020. The relationship of the parameter of ambulatory blood pressure monitoring and bio-marker in women with hypertensive disorders of pregnancy

This paper deals with a sensor fault detection approach using the Optimal Upper Bounded Interval Kalman Filter (OUBIKF) and an adaptive degree of freedom χ2-statistics method. It is devoted to discrete time linear model subjected to mixed uncertainties in terms of observations and noises. Mixed uncertainties mean both bounded and stochastic uncertainties. The degrees of freedom of this χ2 hypothesis test method are adaptively chosen thanks to amplifier coefficients improving the detection of the sensor faults. The proposed approach is an extension of a result developped in Lu et al. (2019). Application on a vehicle bicycle model highlights the efficiency of the proposed approach. Comparisons with other efficient estimation and fault detection strategies are provided to discuss the accuracy of the obtained results.

Path-following enhancement of an 8×8 combat vehicle using active rear axles steering strategies

Active rear steering has been used in many research work to enhance ground vehicles’ lateral stability. However, there is a shortage in the published research studies that consider the incorporation of active rear steering for autonomous vehicles applications, especially in case of multi-axle combat vehicles. In this paper, various H∞ controllers are developed to actively steer rear axles of a multi-axle combat vehicle using a linearized bicycle model. The proposed controllers are incorporated with a 22 degrees of Freedom nonlinear Trucksim full vehicle model to study and compare the developed controllers’ performance on a hard surface. Moreover, a frequency-domain analysis is conducted to investigate the influence of the active rear steering on the path-following controllers’ robustness in terms of stability and performance. Three path-following controllers are designed, where the first controller is applied on the front two axles of the vehicle, while the rear two axles are fixed. The second is applied to all-wheel steering vehicle. The third controller is an integration between the designed front steering path-following controller and a developed lateral stability active rear steering controller. Eventually, a series of virtual maneuvers are performed to evaluate the effectiveness of the intended controllers to present the advantages and limitations of each controller at different driving conditions.

Self-Steering Characteristics of FSAE Vehicle with a Linear Bicycle Model

A FSAE car must exhibit precise and predictable handling behaviour since it is subject to driving manoeuvres in dynamic conditions. Therefore, an accurate prediction of its self-steering characteristics becomes vitally important, especially in the expected lateral acceleration operating range. The simulation implements a linear bicycle model of FSAE car in MATLAB and establishes the understeer gradient and the critical speed, thereby aiding the analysis of the steering wheel angle variation required to negotiate the corners of increasing dynamics.

Control of a Scaled Vehicle in and Beyond Stable Limit Handling

In this research a controller is developed that can control path-tracking both within and beyond stable limit handling. The controller is based on the equations of motion of the nonlinear bicycle model. The performance of the controller is evaluated in simulation, a sensitivity analysis is performed and the controller is implemented on a 1/10 scale radio controlled car. The controller is able to track a path in normal driving conditions and let the vehicle enter and maintain a drift while remaining close to the desired path.

Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects

Vulnerable road users (VRUs)-related crashes are recognized as an important public safety problem. However, few macro-level studies of VRUs-involved crashes have considered the long-term spatial, temporal, or spatiotemporal effects in the crash risk. This study analyzes the bicycle and pedestrian crash risk in different injury severities by using three multivariate Bayesian space–time models. These models address different spatiotemporal effects to account for possible correlations across injury severities over space and time. Various explanatory variables are used to examine the contributory risk factors, including socio-demographic features, roadway structures, and weather characteristics. Spatio-temporal conditional autoregression with an ANOVA style (ST-CARanova) models outperform other two space–time models in most circumstances. The long-term spatiotemporal effects, such as relatively high temporal autocorrelations, significant spatial heterogeneity, and weak spatiotemporal interactions, are found in this study. The increase of female ratios, young people ratios, unemployment rates, and annual average high temperatures could increase the county-level crash risk of cyclists and pedestrians. The findings provide useful insights for policy makers to improve the safety of cyclists and pedestrians.

Handling Enhancement of Autonomous Emergency Steering for Reduced Road Friction Using Steering and Differential Braking

Steering has more potential than braking to prevent rear-end collisions at higher relative velocities. A path tracking controller based on multi-input multi-output (MIMO) model predictive control (MPC) is proposed to enhance the handling performance of autonomous emergency steering in this paper. A six-state MIMO bicycle model including actuator dynamics of steering and differential braking is used for model prediction. Two control inputs are front wheel steering angle and direct yaw moment. Two model outputs are lateral displacement and heading angle. According to the work load ratios at four wheels, control allocation is used to determine the optimal braking force distribution to prevent tire force saturation. The performance of a single-input single-output (SISO) MPC that uses only steering angle control to track the lateral displacement of the desired path is employed to benchmark the performance of the proposed algorithm. Simulation results show that both SISO MPC and MIMO MPC can track the path on nominal road surface with high road friction coefficient of 0.9. For a road surface with medium road friction coefficient of 0.7, the SISO MPC is unable to track the path and loses directional stability. However, the MIMO MPC can still track the path and demonstrate robust path tracking and handling enhancement against model uncertainty due to reduced road friction.

Robust steering control for trajectory following in road traffic environments

The process of modelling vehicle motion in a road traffic environment requires the integration of trajectory generation with vehicle control. The steps involved here are generating a feasible trajectory based on the existing traffic and tracking the trajectory to control it with a steering angle input. Since the parameters of a physical system vary with changes in operating conditions, it is important to consider robustness when designing controllers. This article aims at developing a trajectory-following model with robust steering control strategies to accurately follow a generated trajectory. In this study, performance-based proportional, robust proportional and sliding mode control strategies are designed for trajectory following. The robustness of the proportional controller is established using Kharitonov’s theorem, which is compared with a proportional controller tuned for performance. Sliding mode control is designed for robustness and chattering elimination using two kinds of reaching laws – a constant reaching law and a novel power rate exponential reaching law. The controllers are designed using a dynamic bicycle model considering the error with respect to the trajectory. The controllers are then evaluated in IPG CarMaker®. The resulting trajectories and control inputs are compared for the considered control methodologies using the ISO double lane change and the Slalom tests. Sliding mode control with power rate exponential reaching law is concluded to be more robust as compared to the other controllers, with lower response times, up to 84% lower heading angle deviations from the trajectory and an overshoot of only 3.2% in lane changing.

Vehicle Localization Using Wheel Speed Sensor (WSS) and Inertial Measurement Unit (IMU)

&#x0D; &#x0D; &#x0D; The advent of autonomous driving has led researchers toward a whole new technological age where vehicle positioning and localization system form the back bone of an autonomous electric vehicle. However, localization becomes poor as a vehicle enters GPS-denied areas due to multi path errors. Autonomous vehicle, in addition, needs to be localized from time to time and be guided on the right path along its destination. The purpose of this study is to overcome the problem of adopting an alternative method by using the vehicle’s Wheel Speed Sensor (WSS) for localization. WSS as an auxiliary sensor is attached to the vehicle’s wheel to track its position upon considering its travelling speed in a period of time. This is done in such a way that the existence of obscured portion along the guideway will be neglected. The data obtained from WSS are combined with yaw rate from an Inertial Measurement Unit (IMU) through Kinematic Modelling algorithm and then be converted to get the local position coordinates. In order to analyse whether the yaw rate produced by IMU is acceptable or not, comparison with simulation is needed. A Bicycle Model is used to generate simulated yaw rate from the steering angle of the vehicle and Kalman Filter estimates the simulated yaw rate to be close with the raw yaw rate. Therefore, this will clarify that the yaw rate obtained from IMU is acceptable and that true localization path is generated.&#x0D; &#x0D; &#x0D;