Advanced Vehicle Control Systems Research Articles

Improvement of Traction Force Estimation in Cornering through Neural Network

<div>Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels. The second network utilized longitudinal slip ratios of the driving wheels and longitudinal and lateral accelerations of the vehicle as inputs. The training of the neural networks was performed using data from straight-line accelerations, circuit maneuvers, and a sinus steering maneuver. Both neural networks were designed as multi-output networks capable of simultaneously estimating longitudinal force errors for both driving wheels. The estimator was tested by making two laps on the Hockenheim circuit in the opposite direction. The initial root mean square error (RMSE) was substantially reduced using corrective neural networks. These findings affirm the effectiveness of the neural network-based approach in improving traction force estimation under combined slip conditions, overcoming the limitations of the Pacejka formula in cases of non-pure slip, thereby paving new avenues for the implementation of more advanced and secure vehicle control systems.</div>

Effect of Human Response on the Effectiveness of Advanced Vehicle Control Systems (HRAVCS)

This work aims to investigate the effect of driver response to relayed messages through Human Machine Interface (HMI) on the effectiveness of Human-Vehicle Interface (HVI) and to enable the optimized design of HMI. The investigation and mathematical modeling cover vehicles with Advanced Vehicle Assistant System (ADAS), which operates using Advanced Vehicle Control System (AVCS). The presented model uses driver response time and machine (electronics, sensors, processors) processing time to measure vehicular efficiency and driver interaction, which is also a function of the HMI design, and the way messages are passed to the driver. The produced model uses a probability function that can be used in the design and testing process to assess the effect of failure on the designed interface and relates the function to the driver's response time ratio and the vehicle electronics' processing time. The presented work concluded that as the driver response time increases, effective interaction decreases as a probability function. Also, as the driver response time deviates from the specified threshold, the effective interaction decreases, which also applies to the processing time. In addition, as the time ratio between the driver responses to the machine processing (Time Ratio) increases, the effective interaction parameter (R) value decreases. The work also proved that a more adaptive model is possible using a probability function correlated to the response time ratio to processing time.

Effect of Driver Response on Efficiency of Vehicular Communication using Penalty Cost Function (EVCPCF)

Background and Objective: This study examines and takes into account three key timing factors that have an impact on the effectiveness of human-machine interfaces (HVI). A threshold-based mechanism is created to account for both cooperative driving and advanced vehicle control system (AVCS) scenarios. For AVCS and cooperative driving, the developed model takes into account on-board machine interface time, human interface time, and transmission time. Methods: A threshold function that represents the penalty cost of a slow driver reaction is presented in order to enable adaptive intelligence, enhance HVI design, and increase vehicle safety. The Penalty Cost Function (PCF) is used to make vehicle control systems intervene and take control in situations where the driver responds slowly to safety and warning messages. Additionally, this study demonstrates that AVCS-based vehicular systems are more responsive overall and are less impacted by the PCF function than cooperative systems. Results: The mathematical models created through this work allowed for a limiting efficiency value and capping for each driving scenario, according to comparative plots. This will improve the creation of more reliable control systems as part of a vehicle's mechatronics, impacting how vehicles communicate with one another in a cooperative setting. MATLAB simulation was used to verify the mathematical model. The simulation covered two limiting cases of 0.33 and 0.5 and used incrementing numbers of vehicles (10, 20, 30, 40, 50) to check the impact of increasing vehicle numbers on communication efficiency and examine whether both AVCS and AVCS with cooperative will have close levels and converge at limiting values. Conclusion: The successfully completed simulation demonstrated that throughput decreased as the number of vehicles increased, although in the limiting case, both scenarios and the driving system changed virtually by the same percentage.

Fault Tolerance Analysis of Car-Following Models for Autonomous Vehicles

Microscopic car-following models can be applied in Autonomous Vehicles (AVs) to control the real-time longitudinal interactions among individual vehicles. Moreover, they can have a vital role in Advanced Vehicle Control and Safety Systems (AVCSSs) such as collision warning, adaptive cruise control, lane guidance driver assistance, brake assist as well as in modeling simulation of safety studies and capacity analysis in transportation science. In reality, sensor measurements are generally inaccurate. Surprisingly no comparative assessment of the car-following models in presence of sensor (measurement) errors for AVs exist till now to the best of our knowledge. Therefore, in this paper we evaluate the prominent car-following models for Avs in presence of sensor errors in terms of safety, trip times, flow and fuel efficiency through simulations. We show that sensor errors significantly impact safety and flow in all models, while they do increase trip times and fuel consumption of some of the models. None of the models is completely fault-tolerant and suitable for AVs as some models produce collisions and/or negative velocity while all models violate traffic light. Nonetheless, the k-leader Fuel-efficient Traffic Model (kFTM) is the most fault-tolerant negative velocity and collision free model having reasonable trip times and energy consumption among the investigated models.

Feasibility of Advanced Vehicle Control Systems (AVCS) for Transit Buses

In the course of developing automated vehicle-roadway systems, opportunities to deploy vehicle control systems at intermediate stages of development may emerge. Some of these systems may provide a significant efficiency or safety enhancement to existing operations with manually-driven vehicles. Under certain circumstances, transit buses provide an ideal testbed for such systems. The work presented here represents a feasibility study for the application of Advanced Vehicle Control Systems (AVCS) to transit bus operations. The paper explores past and present research relevant to automatic control for buses and describes specific operations that could be better performed by AVCS-assisted or controlled vehicles.The study concludes with a series of recommendations for proceeding toward a deployment phase. For transit bus operations, the most suitable deployment opportunities for AVCS exist on exclusive busways (bus-only roads) or large bus servicing facilities used for daily maintenance operations. Busways would provide an excellent testing ground for a lateral control/lane keeping system. Such a system would provide immediate utility on the existing busway and would serve as a building block for more highly automated systems in the future. Maintenance operations in service garages require dedicated drivers to move vehicles through a routine servicing sequence. By fully automating the movement of buses within such facilities, labor costs could be dramatically reduced.

Gain-Scheduled Steering and Braking Coordinated Control in Path Tracking of Intelligent Heavy Vehicles

Abstract In the context of intelligent transportation system and advanced vehicle control system, higher requirements for intelligent control and coordinated control of heavy vehicles (HVs) have been proposed. Automatic path tracking control is essential for automatic driving of heavy vehicles. However, uncertain lateral disturbances and time-varying characteristics of system parameters make it difficult to guarantee roll stability and path tracking accuracy during automatic path tracking. This paper investigates the coordination of active front steering (AFS) and direct yaw moment control (DYC) in the automatic path tracking system for intelligent heavy vehicles. The main idea is to adjust the braking action according to the rollover risk evaluated by a specific rollover index (RI). The coordination of steering and braking systems and the robustness of the system against time-varying parameters are achieved by a gain-scheduled linear parameter varying (LPV) controller. Based on the linear matrix inequality (LMI) framework, the LPV controller is synthesized to ensure the robust H∞ performance against external disturbances. Simulation results show that the proposed gain-scheduled LPV/H∞ control strategy can enhance roll stability and path tracking accuracy in the path tracking process of intelligent heavy vehicles.

Intelligent vehicle control at signal‐free intersection under mixed connected environment

Conflict vehicle movements at intersections are one bottleneck of traffic mobility on urban networks. Recently, with the development of connected vehicles, regulating conflict movements by controlling individual vehicles to improve intersection performance has attracted a lot of research efforts. This study aims at developing an advanced vehicle control system with connected vehicles to reduce vehicle delays at intersections without the help of signals. An intersection delay model is proposed to predict the total vehicle delay at one intersection and to search for the optimal speed control actions to manage traffic under mixed connected and non-connected vehicle environment. The simulation experiments illustrate that the control system is able to reduce average vehicle delay by up to 95%, and the sensitivity analysis of vehicle demand and market penetrate rates of connected vehicles is also conducted to understand the benefits of the system at different traffic and connected vehicle environments.

Tire-Road Friction Coefficient Estimation under Constant Vehicle Speed Control

Abstract Tire-road friction coefficient (TRFC) is of critical importance for advanced vehicle control systems and autonomous vehicle applications. This paper presents a sequential tire longitudinal stiffness and TRFC estimation method for road vehicles by separate control of the front and rear wheels, for which only standard Kalman filter (KF) and recursive least squares (RLS) techniques are required. While braking torque is actively applied to rear wheels, the proportional-integral-derivative (PID) speed controller dictates the corresponding driving torque to front wheels to compensate the decrease of the vehicle longitudinal speed. Tire force of rear tires are estimated based on the KF, both longitudinal stiffness and TRFC of rear tires are identified using RLS technique. The proposed method has simple structure and the potential of estimating the parameters without severely affecting the vehicle’s desired longitudinal speed. Simulation results from vehicle dynamics software CarSim and Matlab/Simulink validate the effectiveness of the proposed method.

Literature review and fundamental approaches for vehicle and tire state estimation

ABSTRACTMost modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable for enhancing the performance of vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production vehicles are equipped with onboard sensors (e.g. a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors), which when used in conjunction with certain model-based or kinematics-based observers can be used to identify relevant tire and vehicle states for optimal control of comfort, stability and handling. Vehicle state estimation is becoming ever more relevant with the increased sophistication of chassis control systems. This paper presents a comprehensive overview of the state-of-the-art in the field of vehicle and tire state estimation. It is expected to serve as a resource for researchers interested in developing vehicle state estimation algorithms for usage in advanced vehicle control and safety systems.

Realistic Mobility Model of AVCSS (Advanced Vehicle Control and Safety System) for Accident Detection with Tracking Technology

Realistic Mobility Model of AVCSS (Advanced Vehicle Control and Safety System) for Accident Detection with Tracking Technology

Vehicle Side Slip Angle Observation with Road Friction Adaptation

Side-slip angle is an important variable indispensable for some advanced automotive vehicle control systems. However, due to technical and economic reasons, this variable is often estimated rather than directly measured. In this paper we address the observer design problem for estimating the side-slip angle and the unknown road friction coefficient, based on measured signals from sensors common to modern series-production automobiles. We formulate state and parameter estimation as a non-convex optimization problem. By interweaving discrete time solution of the optimization and continuous integration of sensor data, our scheme allows for sufficient time for finding the global optima approximately through a grid-search. Consequently, despite the non-convex optimization we are facing, our observation scheme is able to run in realtime. We show some desirable properties of the proposed scheme concerning the stability and convergence of estimation error. One advantage of our observer is that for the nominal model the estimation error does not grow even when the system lacks observability. Simulation shows that the proposed observer provides very accurate estimation in challenging scenarios where the vehicle executes extreme maneuvers and measured signals are corrupted by noise.

Fast-Optimized Object Detection in Dynamic Scenes Using Efficient Background Weighting

Moving object detection is an important fundamental process in intelligent vision systems and an essential preprocessing step in high-level machine vision applications such as object tracking and moving analysis. This technique helps to detect suspicious events in video monitoring and is a key process for concentration estimation in traffic management. It is also one of the methods used in advanced vehicle control systems to keep vehicle in path and prevent accidents.In this paper, an effective weighted background moving object detection is presented,which is optimized for scenes with dynamic background. The proposed detection is based on real time background subtracting with high accuracy, low computational complexity and a short processing time, which makes it a good candidate for hardware implementation. The proposed algorithm is simulated in MATLAB software. The simulation results in MATLAB on various image sequences and comparison with mixture Gaussian method and median filter algorithm shows the effective weighted background method has better performance in different evaluation criteria that approves its efficiency in dynamic scenes.

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

The recent progress of advanced vehicle control systems presents a great opportunity for the application of model predictive control (MPC) in the automotive industry. However, high computational complexity inherently associated with the receding horizon optimization must be addressed to achieve real-time implementation. This paper presents a generic scale reduction framework to reduce the online computational burden of MPC controllers. A lower dimensional MPC algorithm is formulated by combining an existing “move blocking ” strategy with a “constraint-set compression” strategy, which is proposed to further reduce the problem scale by partially relaxing inequality constraints in the prediction horizon. The closed-loop stability is guaranteed by adding terminal zero-state constraint. The tradeoff between control optimality and computational intensity is achieved by proper design of the blocking and compression matrices. The fast algorithm has been applied on intelligent vehicular longitudinal automation, implemented as a fuel economy-oriented adaptive cruise controller and experimentally evaluated by a series of real-time simulations and field tests. These results indicate that the proposed method significantly improves the computational speed while maintaining satisfactory control optimality without sacrificing the desired performance.

GPS based Advanced Vehicle Tracking and Vehicle Control System

Security systems and navigators have always been a necessity of humans life. The developments of advanced electronics have brought revolutionary changes in these fields. In this paper, we will present a vehicle tracking system that employs a GPS module and a GSM modem to find the location of a vehicle and offers a range of control features. To complete the design successfully, a GPS unit, two relays, a GSM Modem and two MCU units are used. There are five features introduced in the project. The aim of this project is to remotely track a vehicles location, remotely switch ON and OFF the vehicles ignition system and remotely lock and unlock the doors of the vehicle. An SMS message is sent to the tracking system and the system responds to the users request by performing appropriate actions. Short text messages are assigned to each of these features. A webpage is specifically designed to view the vehicles location on Google maps. By using relay based control concept introduced in this paper, number of control features such as turning heater on/off, radio on/off etc. can be implemented in the same fashion. to the user with the location coordinates (i.e. Longitude and Latitude). These coordinates can be used to view the location of a vehicle on Google maps. The vehicle tracking system presented in this paper comprises of a cost effective and special tracking technology. It offers an advanced tracking and a variety of control features that facilitate the monitoring and clever control of the vehicle. The tracking systems are not only bounded to shipping industry and fleet tracking but also used in cars as a theft prevention tool. This paper provides an overview of the background research related to vehicle tracking and control systems, choice and full development process of the tracking system. The paper is divided in five main sections: related research, choice of components, design of a system, simulation of designs and implementation process. In the related research section, we will outline the research carried out so far. Then, we will discuss the components used. The design section will focus the software and hardware design process. The assembly of components will be discussed in the implementation section. Finally, the implementation process section will include the software simulations and images of the hardware in working condition.

Variable structure controller design for steer-by-wire system of a passenger car

The electric power steering (EPS) system was developed and the steer-by-wire (SBW) system achieves the purposes of EPS. The advantages of SBW are packaging flexibility, advanced vehicle control system, and superior performance. No mechanical linkage exists between the steering gear and steering column in the SBW system. The steering wheel and front-wheel steering can be controlled independently. The SBW system consists of two motors controlled by an electronic control unit (ECU). One motor is in the steering wheel and develops the steering feel of the driver and the other motor is in the steering linkage and improves vehicle maneuverability and stability. Moreover, the active front steering (AFS) system can be added to the SBW system. AFS reduces the difference between actual and estimated vehicle yaw rate. Up-to-date information from the steering wheel enables drivers to identify road conditions through the tire force, which should be fed back to the steering wheel. Furthermore, several control algorithms related to the vehicle and motor can be used together through the self-aligning torque, which is fed back to the steering wheel. This study proposes a method to control the vehicle yaw rate through an SBW system. This control method is based on a PID control method for the steering-wheel-motor controller, as well as on a sliding mode control (SMC) method for the front-wheel-motor controller and yaw stability controller. The SBW system is modeled using a bond graph method. Results imply that the controllers are robust enough when in contact with nonlinear properties of tire and road conditions. This study is expected to guide further research on the SBW system.

Safety Driving Decision-Making of the Advanced Vehicle Control and Safety System

ITS deployment was adopted to improve traffic operation. Actuated control and advanced traffic management have been deployed in the project area. Driving is a multitasking activity that requires drivers to manage their attention between various driving-and non-driving-related tasks. This paper discusses the problem of modeling realistic and human-like behaviors on simulated highway systems by developing a framework. This framework consists of four units that are the perception unit, the emotions unit, the decision-making unit, and the decision-implementation unit. This paper has researched on the automatic vehicle driving mode (The Advanced Vehicle Control and Safety System) at the four-lane highway. Automated vehicle decision-making procedure is discussed in this research. Two safety indices of automate vehicle driving are explored. In real experiment, TBC index may be good for commending evaluating automate vehicle driving behavior.

Vision Based Autonomous Lateral and Longitudinal Control System

Automation combined with the increasing market penetration of on-line communication, navigation, and advanced driver assistance systems will ultimately result in Intelligent Vehicle Highway Systems. Advanced Vehicle Control Systems (AVCS) is a key technology for Intelligent Transportation System (ITS) and Intelligent Vehicle Control System (IVCS). The unmanned control of the steering wheel is one of the most important challenges faced by the researchers. This paper proposes 1D sensor calibration algorithm which makes the vehicle to identify the track independent of various lighting conditions. Pre-processing algorithm helps in finding the position and orientation of the vehicle in the track. A novel control architecture for automatic steering, acceleration and braking control of a self guided vehicle has been proposed which consists of cascaded Kalman filter with PID steer control algorithm. It provides smooth tracking over the entire track. Adaptive speed control based on speed difference and acceleration control algorithm based on steer error are used. These algorithms make the vehicle to optimally manoeuvre the track. The experimental results show that the combination of Kalman filter with PID for lateral control reduces trajectory error to a minimum level and adaptive speed control algorithm for longitudinal control provides smooth speed over the entire track.

경사진 노면에서의 차량의 종 속도 추정

Abstract : On-line and real-time information of the longitudinal velocity is the essential factor for the Advanced Vehicle Control Systems such as ABS(Anti-lock Brake System), TCS(Traction Control System), ESC(Electronic Stability Control) etc. However, the longitudinal velocity cannot be easily measured or calculated during braking maneuvering. A new algorithm is presented for the estimation of the longitudinal velocity with the measurements of the vehicle longitudinal/lateral acceleration, steering angle and yaw rate. The algorithm is designed utilizing the Extended Kalman Filter based on the 3 degree of freedom vehicle model. In order to compensate for the biased sensor signal on the inclined road, the inclined angle is also estimated. The performance of the proposed estimation algorithm is evaluated in field tests. Key words : Longitudinal velocity(종 속도), Inclined angle estimation(경사각 추정), Longitudinal acceleration compensation(종 가속도 보정), Extended kalman filter(확장된 칼만필터) Nomenclature

Implementation of AVCS using Kalman Filter and PID Controller in Autonomous Self Guided Vehicle

Advanced Vehicle Control Systems (AVCS) is a key technology for Intelligent Transportation System (ITS) and Intelligent Vehicle Control System (IVCS). AVCS involves automatic steering, acceleration and braking control of fully autonomous vehicles. The unmanned control of the steering wheel is one of the most important challenges faced by the researchers. This paper proposes control architecture for automatic steering, acceleration and braking control of a self guided vehicle. Self guided vehicle is a line follower which tracks a black line on a white surface, through an array of infrared sensors. In line following, the readings that show deviation from the line are considered as lateral error and the proposed algorithm works towards minimizing the lateral error. Calculation of the lateral error and filtering the error values using Kalman filter is the first level of error calculation. Kalman filter protects the steering action from erroneous sensor values. PID control algorithm uses the filtered value and calculates the required steer to achieve the zero lateral error. The proposed adaptive speed control algorithm uses the speed boosting techniques for the longitudinal control of the vehicle. The experimental results show that the combination of Kalman filter with PID for lateral control reduces trajectory error to a minimum level and adaptive speed control algorithm for longitudinal control provides smooth accelerations over the entire track. Thus proposed algorithm gives better performance in both the lateral and longitudinal control.

Improvement of a Human-Machine Interface (HMI) for Driver Assistance Using an Event-Driven Prompting Display

Visual display systems showing lateral roadway position of a vehicle have been used in advanced vehicle control and safety systems for driver assistance in hazardous conditions, e.g., in snowplowing operations. A simple simulation of lateral control stability of the vehicle indicates the adverse effect of dead-time resulting from the driver glancing back and forth between windshield and display. An event-driven prompting display is presented to reduce the effect of the dead-time and improve the safety of the driver assistance system. Test results in actual driving provide a comparison of the performance of the new display versus the original display system, and clearly indicate the improvement in lateral stability in driving a vehicle.