The inadequate effect of automobile seating on foot posture and callus development

Driver posture is an important factor to be considered in the ergonomics design process of automobiles. Most decisions during automobile design and manufacture are informed by studying the intricate biomechanical components of human musculoskeletal systems to ensure maximum comfort, safety and well-being during driving. A case study is presented that confirms inappropriate foot position as a causative factor for the development of abnormal lateral/plantar heel callosities when driving a 4 x 4-style vehicle. The driver's foot position was influenced by the seat geometry of the vehicle. Cessation of driving the 4 x 4-style vehicle and driving of an alternative automobile while on holiday for a period of 4 weeks resolved the condition. On return to the 4 x 4-style vehicle, however, the abnormal callus patterns redeveloped while using the same footwear and no change in any other parameters. It is therefore suggested that seat and consequent foot position is an important ergonomic factor that should be addressed in the future design of automobile seating.     

Model predictive control-based fault detection and reconstruction algorithm for longitudinal control of autonomous driving vehicle using multi-sliding mode observer

This paper presents a model predictive control-based fault detection and reconstruction algorithm for longitudinal control of autonomous driving using a multi-sliding mode observer. In order to secure the safe longitudinal control of a vehicle, a numbers of factors must be ensured, such as the reliability of the longitudinal information, the data on the forward object from the environment sensor, and the acceleration of the ego vehicle. Thus, we propose a reasonable failure detection scheme for the acceleration signal of the host vehicle and the relative values of the front object of the radar. In order to identify the faults of the radar and the vehicle acceleration sensor related to the automated longitudinal control, the multiple sliding mode observer and prediction of model predictive control (MPC) algorithm are applied. The relative acceleration is reconstructed by applying a sliding mode observer (SMO) with clearance and relative speed measurements. The upper and lower limits of longitudinal acceleration were computed by analyzing human driving data under the preceding vehicle and reconstructed acceleration. A proper acceleration range can be defined precisely based on several reconstructed upper and lower bounds by using a multiple sliding mode observer with stored prediction data of relative values, making it possible to effectively identify the fault of the host vehicle’s acceleration sensor. By applying MPC for this study, optimal control input and prediction of relative states can be obtained that are more reasonable than those using the linear prediction model. The proposed fault detection algorithm can identify the abnormal state of the environment sensors by using the accumulated past sensor data. By comparing the stored prediction of relative states with the stored data on current states for a given period, the signal faults of the longitudinal target information can be detected from environment sensors. With these fault indices of states, the final fault diagnoses of sensors can be determined by assessing confidence through statistical analysis of 27 sets of normal driving data. In order to obtain a reasonable performance evaluation, this study uses actual driving data and a 3D full vehicle model constructed in the MATLAB/Simulink environment. The test results reveal that the proposed algorithm can successfully detect the fault of the radar and acceleration sensor of the automated driving vehicle.

     

Mixture of Automatically- and Manually-controlled Vehicles in Intelligent Transport Systems

This paper presents a technology which allows for the existence of mixed traffic, as a first step towards intelligent transport systems. We begin by designing an automatic driving controller called the intelligent vehicle driving system (IVDS). This is a two-layer system: the higher layer analyzes the current scenario and infers the control objective that associates with a certain index function; the lower layer optimizes the function provided by the upper layer. IVDS only uses the measurement of a vehicle's speed and distance relative to the vehicle in front, together with measurements of the vehicle's own state. Consequently, the vehicles equipped with the IVDS can operate together with manually-controlled vehicles. Next, a mathematical rule-based model for human drivers is developed. This model attempts to mimic human driver's behavior in vehicle following and lane-changing. Finally, we examine the control performance of the proposed controller and the potential benefits of mixed traffic by implementing the human driver model and IVDS on an automated highway simulator.     

基于车载传感信息融合的电动汽车驾驶行为辨识

本文的主要目的是基于信息融合的方法设计出一套能准确辨识出驾驶行为的系统.本系统使用六轴加速度计采集加速度信息,通过多尺度多重分形(MMA)算法(首次将该算法用作特征提取的方法)从加速度信号中提取出可反映不同驾驶行为的波动特征.并采集电动汽车的OBD接口获取的包括速度、功率、电流等车载OBD信息并提取特征.分别通过随机森林(RF)算法对驾驶员的驾驶行为进行辨识.提出一种新的信息融合的方法,采用该方法对加速度信息和OBD信息进行融合,发现信息融合的方法可以更有效的辨识出电动汽车的驾驶行为.     

基于LonWorks现场总线的交通信号控制系统

利用LonWorks现场总线技术，设计开发了一种基于现场总线的交通信号控制系统，将信号控制器、LED信号灯、倒计时器及检测器等作为LON节点组成一个完整的网络系统，能够有效地完成灯色控制、LED灯故障检测、绿冲突检测、倒计时驱动及交通量检测等任务。现场应用结果表明：LonWorks现场总线技术的应用可大大提高交通信号控制系统的稳定性和可靠性。     

基于预测控制的高超声速飞行器容错控制器设计

针对存在升降舵面偏转角卡死故障的高超声速飞行器,提出一种基于预测控制的容错控制器设计方法.利用输入输出反馈线性化,对高超声速飞行器纵向模型进行变换;对于速度和高度的高阶导数以及故障项,设计扩张状态观测器在线观测;采用泰勒展开得到预测模型,建立连续预测控制器,分析证明闭环控制系统的稳定性和观测误差的有界性.仿真结果验证了所提方法的有效性.     

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features.     

基于模糊推理的汽车辅助驾驶系统控制算法

在建立汽车辅助驾驶系统模型的基础上，指出满足驾驶员的驾驶特征是车辆控制的一个重要指标，此外针对驾驶员驾驶行为的不精确性，提出了以模糊推理为基础的上位控制方法，并对其进行了现场实验。实验结果表明，用模糊控制理论模拟驾驶行为的不精确性是可行的。通过模糊控制自车的速度，能够实现自车在多种工况下保持安全状态。     

Nonlinear feedback control and trajectory tracking of vehicle

This paper mainly studies nonlinear feedback control applied to the nonlinear vehicle dynamics with varying velocity. The main objective of this study is the stabilisation of longitudinal, lateral and yaw angular vehicle velocities. To this end, a nonlinear vehicle model is developed which takes both the lateral and longitudinal vehicle dynamics into account. Based on this model, a method to build a nonlinear state feedback control is first designed by which the complexity of system structure can be simplified. The obtained system is then synthesised by the combined Lyapunov–LaSalle method. The simulation results show that the proposed control can improve stability and comfort of vehicle driving. Moreover, this paper presents a lemma which ensures the trajectory tracking and path-following problem for vehicle. It can also be exploited simultaneously to solve both the tracking and path-following control problems of the vehicle ride and driving stability. We also show how the results of the lemma can be applied to solve the path-following problem, in which the vehicle converges and follows a designed path. The effectiveness of the proposed lemma for trajectory tracking is clearly demonstrated by simulation results.     

有界扰动下异质车辆队列分布式鲁棒经济预测控制

针对有界扰动下异质车辆队列节能与稳定分布式协同控制问题,提出一种新的分布式鲁棒经济模型预测控制(economic model predictive control, EMPC)策略.首先采用不确定误差模型描述有界扰动下异质车辆队列纵向行驶动态特性,再应用tube思想对系统约束进行紧缩设计,补偿有界扰动对系统造成的不确定性.其次,采用局部车辆行驶能耗模型描述车辆队列分布式经济性能优化的有限时域最优控制问题,并利用传统跟踪性能指标设计附加稳定收缩约束函数.进一步,基于系统收缩原理,建立车辆队列闭环系统关于有界扰动的输入-状态稳定性条件.最后,通过与车辆队列传统分布式鲁棒模型预测控制策略的数值仿真对比结果验证了所提出策略的有效性和优越性.     

基于神经网络滑模的智能车辆横向控制

以智能车辆为研究对象,针对车辆模型存在高度非线性动态特性、参数不确定性以及行驶时受外部干扰较多导致控制精度不高、鲁棒性差等问题,提出了采用径向基函数(RBF)神经网络滑模控制方法.建立2自由度线性车辆模型和自由度非线性整车模型,在传统2自由度车辆控制模型状态方程的基础上推导出新的状态方程并以此设计了相应控制器.利用李雅普诺夫(Lyapunov)稳定性理论推导出神经网络的权,并证明控制系统的稳定性.仿真结果表明:与传统的滑模控制方法相比,该方法控制精度高,有较强的鲁棒性.     

驾考计时预约系统开发与设计

随着信息化社会的不断深入,各行各业都在加快信息化的进程。为了更好适应民众方便自由的出行,驾驶技能成为大众迫切需要的技能。传统的机动车驾驶证的培训基本都在线下完成,教练的选择、时间的确定、场地的安排等都需要现场交涉,耗费民众大量的时间。在学习过程中也存在出勤率不高、练习时间不足等人为因素导致的缺陷。这些都给机动车驾驶证的培训效率和质量带来不足,为此需要对传统的驾考系统进行改革。本项目提出驾考计时预约系统的设计,主要解决上述不足,提高培训的效率和质量。为维护交通安全、文明、有序,从源头对驾驶人进行管理,严把入口关,规范培训制度,同时实现驾考系统的信息化。     

Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.     

Design,tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance

This paper describes the design, tuning, and evaluation of a full-range adaptive cruise control (ACC) system with collision avoidance (CA). The control scheme is designed to improve drivers’ comfort during normal, safe-driving situations and to completely avoid rear-end collision in vehicle-following situations. Driving situations are divided into safe, warning, and dangerous modes. Three different control strategies have been proposed, depending on the driving situation. The driving situations are determined using a non-dimensional warning index and the time-to-collision (TTC). The control parameters of the proposed ACC/CA system are tuned by a confusion-matrix method using manual-driving data in no-crashing driving situations. The vehicle-following characteristics of the subject vehicle were compared to real-world, manual-driving data. Finally, the ACC/CA system was also implemented in a real vehicle and tested in both safe-traffic and severe-braking situations. It is shown that the proposed control strategy can provide natural following performance that is similar to human manual-driving in both high-speed driving and low-speed stop-and-go situations. Furthermore, it can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

Enhanced postural stability following driver training is associated with positive effects in vehicle kinematics during cornering

OBJECTIVE: The purpose of the study was to examine the effects of a specific post-license driver training program on postural stability and vehicle kinematics during cornering. BACKGROUND: Inertial forces experienced during driving can perturb a driver's posture, which may in turn diminish a driver's perceptual sensitivity and corresponding control actions. METHODS: A trainee group (n=21) and control group (n=12) participated in the study. The trainee group participated in a 2-day driver training program that included instruction on how to enhance perceptual sensitivity, postural stability, and vehicle kinematics during common driving maneuvers, including cornering. Postural stability and vehicle kinematics were assessed during cornering maneuvers performed on a closed-circuit track using an instrumented vehicle prior to and following training. RESULTS: Trainee drivers experienced enhanced postural stability and reduced the magnitude and onset of peak vehicle lateral accelerations following training. Prior to training, drivers who were more posturally unstable tended to experience higher lateral vehicle accelerations, and drivers with the biggest improvements in postural stability following training tended to experience the greatest reductions in lateral accelerations of the vehicle. CONCLUSION: Training led to changes in postural stability that were associated with reduced lateral accelerations during cornering. APPLICATION: The reduction in lateral accelerations following training in the present study indicates a greater dynamic margin of safety for cornering. Overall findings suggest that the driver training programs produced beneficial effects on cornering kinematics and that these effectswere associated with enhanced postural stability.     

Fault detection of actuator faults in unmanned underwater vehicles

A fault-diagnostic system for unmanned underwater vehicles has been designed and tested in real operating conditions. Actuator faults have been considered, relying on approximate models of the vehicles’ dynamics. Fault detection and diagnosis is accomplished by evaluating any significant change in the behaviour of the vehicle. This task is performed by a bank of estimators: a filter is implemented for each actuator fault type, including the no-fault case. The estimators used are extended Kalman filters (EKF), due to the presence of nonlinearities in the dynamic models. Experimental results are reported, to demonstrate the effectiveness of the proposed approach.     

Personal driving diary: Automated recognition of driving events from first-person videos

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person’s daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.     

基于车辆检测技术的智能交通控制器设计

针对复杂多变的交叉路口交通流信号灯控制问题设计了一种基于感应式车辆检测技术的多相位智能交通控制器．该控制器支持6种控制算法，可实现8种工作模式，能满足大部分复杂类型交叉路口的控制需求．同时，控制器具有绿冲突和故障检测功能，以及在故障情况下的降级处理机制，从而在最大程度上保证交通的安全和畅通．提出了一种基于车辆等待长度的单路口多相位交通信号灯自适应控制策略，该算法在保证交通畅通的情况下，使车辆延误时间尽可能的短．该控制器目前已在杭州等地进行了实际应用，结果令人满意．     

汽车驾驶模拟器转向盘回正系统设计

在汽车驾驶模拟器的研发中,对于转向盘回正系统的模拟,可以增强驾驶员操作时的触感,使交通安全实验数据更加可靠.从转向盘回正力矩数学模型研究出发,采用直流力矩电机作为模拟系统中回正力矩的生成部件,设计了整个转向盘回正模拟系统的组成,提出了相应的控制策略.     

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

This paper proposes a behaviour recognition methodology for ground vehicles moving within road traffic using unmanned aerial vehicles in order to identify suspicious or abnormal behaviour. With the target information acquired by unmanned aerial vehicles and estimated by filtering techniques, ground vehicle behaviour is first classified into representative driving modes, and then a string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Furthermore, a fuzzy decision-making process is developed to systematically exploit all available information obtained from a complex environment and confirm the characteristic of behaviour, while considering spatiotemporal environment factors as well as several aspects of behaviours. To verify the feasibility and benefits of the proposed approach, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.