Multiple semi-active suspension control systems have been studied and adapted to vehicles in the past. Many of these systems work with actual road conditions, while oncoming road conditions are not considered. This paper presents a method to integrate look-ahead road information in the adaptive semi-active suspension control with energy-efficient cruise control. Oncoming road conditions and categories are known by using a global positioning system and historic road information. The control configuration has been designed with the Linear Parameter Varying framework. The behavior of the controller can be modified by the use of a dedicated scheduling variable, which is defined by considering a look-ahead estimation algorithm based on prehistoric simulations of passive suspension. The operation of the integrated adaptive semiactive suspension system is demonstrated through real-time simulation in the TruckSim environment with real geographical data. In order to prove the effectiveness of the proposed method two different simulations have been evaluated and compared: one with conventional semi-active suspension and another with an adaptive semi-active suspension. The simulation results show that the overall performance in road holding, suspension deflection and ride comfort has been improved, which effectively demonstrates the advantage of presented adaptive semi-active suspension control based on look-ahead information.
Several types of dampers are used in the suspension systems of road vehicles, while the magneto-rheological damper is one of the most efficient solutions to manage the balance between objectives of vehicle suspension control. Various types of failures may occur in magneto-rheological dampers, and these faults may affect adversely the operation of the vehicle suspension system. This paper is concerned with the design of semi-active suspension control with fault-tolerant control reconfiguration through the Linear Parameter-Varying control method. In case of a damper oil leakage affecting the damper force, the proposed control strategy calculates feasible damper forces for the healthy dampers in order to compensate for the lost force of the faulty damper. Unlike other approaches in the literature, this method aims to enhance performances by modifying the force of a healthy damper to the same level as the force of a faulty damper. The simulation has been performed in the TruckSim environment to demonstrate the operation of the proposed fault-tolerant control strategy. The results show that the introduced novel reconfiguration control method improved driving comfort, road holding and vehicle stability.
There are several methods and technologies in order to increase the safety operation of autonomous vehicles. One of the most important technology is the braking system, which affects driving safety and comfort. This paper evaluates the braking distance and their calculation methods. Two different methods, which are just velocity and friction-based basic method and vehicle dynamics-based complex method, have been considered. The simulation has been held in PreScan vehicle simulation environment in order to find braking distance for the considered vehicle in order to compare result with other two methods. This simulation results are compared with two methods and results show that braking distance is smaller in lower velocity in the simulation, while simulation results are between these two method in higher velocity.
The aim of this paper is to design a fault-tolerant trajectory tracking control for an autonomous vehicle based on a camera and Global Positioning System (GPS) unit. The trajectory tracking control has been founded on the Linear Quadratic control framework. The camera and GPS units calculate the lateral error of the vehicle. A standard webcam is used for lane detection and the lateral error of the vehicle is calculated by using the Hough Transform. A GPS sensor, which is the TruckSim model is used to calculate lateral error by comparing it with a reference database. In the case of lateral error calculated from the camera system is unstable and increased abruptly, the camera unit is faulty and lateral error that is provided by the GPS unit is reliable. Then, a reliable sensor signal is used to calculate the lateral error of the vehicle in order to perform trajectory tracking.
The TruckSim dynamic vehicle simulation software is used with a real-time Simulink/MATLAB model for validation of this study.
During the simulation, extra distortions have been integrated to perform camera fault and the GPS sensor has been considered as a reliable sensor. Results show that the system has performed successful trajectory tracking with the fault scenario.
Adaptive suspension control considering passenger comfort and stability of the vehicle has been researched intensively, thus several automotive companies already apply these technologies in their high-end models. Most of these systems react to the instantaneous effects of road irregularities, however, some expensive camera-based systems adapting the suspension in coherence with upcoming road conditions have already been introduced. Thereby, using oncoming road information the performance of adaptive suspension systems can be enhanced significantly. The emerging technology of cloud computing enables several promising features for road vehicles, one of which may be the implementation of an adaptive semi-active suspension system using historic road information gathered in the cloud database. The main novelty of the paper is the developed semi-active suspension control method in which Vehicle-to-Cloud-to-Vehicle technology serves as the basis for the road adaptation capabilities of the suspension system. The semi-active suspension control is founded on the Linear Parameter-Varying framework. The operation of the presented system is validated by a real data simulation in TruckSim simulation environment.
This paper introduces an adaptive semi-active suspension control by considering global positioning system-based and historical road information. The main idea of this study is to find a corresponding trade-off between comfort and stability at different road irregularities. The introduced semi-active controller is designed based on the Linear Parameter-Varying framework. The behavior of the designed controller can be modified by the use of a scheduling variable. This scheduling variable is selected by considering the road category, which is defined by the ISO 2631-1 standard. TruckSim simulation environment is used in order to validate the introduced adaptive semi-active suspension control system by comparing it with the non-adaptive scenario. The results show that both driving comfort and vehicle stability have been improved with the proposed adaptive semi-active suspension control.
Several semi-active suspension control systems have been studied and adapted to vehicles in the past. Many of these systems work with actual road conditions, while oncoming road conditions are not considered. This paper presents a method to integrate look-ahead road information in the adaptive semi-active suspension control. Oncoming road conditions and categories are known by using a global positioning system and historic road information. ISO 2631-1 standard was considered to categorize the road with different distortion types based on vibrations acting on the passengers of the vehicle. The adaptive semi-active suspension control is designed using Linear Parameter Varying (LPV) framework. The behavior of the controller can be modified by the use of a dedicated scheduling variable. This corresponding scheduling variable for the adaptive semi-active suspension system is defined by considering the road category. The selection of the scheduling variable considers a look-ahead estimation algorithm based on prehistoric simulations of passive suspension. The operation of the integrated adaptive semi-active suspension system is demonstrated through real-time simulation in TruckSim environment with real geographical data. In order to prove the effectiveness of the introduced method two different simulations have been evaluated and compared: one with conventional semi-active suspension and another with an adaptive semi-active suspension. The simulation results show that the overall performance in road holding, suspension deflection and ride comfort has been improved, which effectively demonstrates the advantage of presented adaptive semi-active suspension control based on look-ahead information.
Semi-active suspension control and vehicle cruise control systems have already been developed by researchers and adapted by automotive companies. Most of these systems react on actual road irregularities and terrain characteristics, and the control for each subsystem is designed separately. However, since oncoming road conditions can be known by using historic road information and GPS navigation system, the paper introduces a method to build in look-ahead road data in the control of the adaptive semi-active suspension, moreover, design the vehicle velocity for the cruise controller considering comfort and energy efficiency at the same time. The operation of the presented integrated suspension and velocity control system is validated by a real data simulation in TruckSim environment.
In this chapter a reconfigurable trajectory -tracking control design method has been presented for autonomous in-wheel electric vehicles with independently controlled hub motors and the steer-by-wire steering system. The high-level control reconfiguration has been implemented through the design of a scheduling variable using the LPV framework in order to deal with fault events, while in normal operating conditions the objective of the reconfiguration is to maximize battery SOC, thus enhancing the range of the in-wheel electric vehicle. The energy optimal control reconfiguration has been designed based on the results of preliminary simulations with a high-fidelity vehicle and electrical models on different road conditions. Finally, the efficiency of the proposed method has been demonstrated in a real-data CarSim simulation, showing significant energy saving by the proposed method
Intelligent Transportation System has been the driving forces to enable the paradigm of autonomous vehicles, smart roads and Internet of Things (IoT). For the safety and security of the traffic and transportation, stabilization of the technology and system is necessary. In addition to this, security of intelligent transportation system also influences the smart security and safety of vehicles, pedestrians and drivers. Thus, it is one of the most important application for the daily technology. There has been significant study related to security in vehicular network systems for intelligent transportation system usages. In this study, smart road and intelligent transportation system terms were explained. Attacks and threats of intelligent transportation system were evaluated with their security solutions while security objectives and architecture of intelligent transportation system and smart road were examined. During the evaluation, The European Telecommunications Standards Institute security standards were considered. It is possible to deduce that with developed technology, attack and threats level will be much bore pre-cariousness. New threats and attacks have to be investigate and simulate to find the solution for them.
Due to development in technology; technological revolution has been occurred in many sectors. The automotive sector is at the head of these technological revolutions. Autonomous vehicle technology and the development of sensors, cameras, radar and decision-making mechanisms under this technology have made the design and development of autonomous vehicles possible for every company. The aim of this study is to analyze the public’s confidence in autonomous vehicles. In this study, driver and pedestrian/passenger trust was analyzed with online survey which was performed with 107 participants. Furthermore, briefly autonomous vehicle market analysis was performed with same survey and same participants. While 60,7% of participants have basics knowledge about autonomous vehicles and their systems, 10,3% of attenders didn’t have any knowledge before. The presence of autonomous vehicles in the traffic is not disturb 73,8% of participants, conversely it can be problem for 6.5% of attenders. 63,5% of participants can drive on same line with autonomous vehicle while 9,6% of attenders do not prefer that. 60,7% of respondents have trust to autonomous vehicle as pedestrian who crosses over. 56,1% of participants prefer domestic produced autonomous vehicle instead of other brand who produces autonomous vehicle and 66,3% of attenders prefer autonomous vehicle in lieu classical vehicle in case of availability. According to this analysis, majority of community in Turkey have positive perspective on autonomous vehicle.