Validation of vehicle dynamics simulation models – a review

In this work, a literature survey on the validation of vehicle dynamics simulation models is presented. Estimating the dynamic responses of existing or proposed vehicles has a wide array of applications in the development of vehicle technologies, e.g. active suspensions, controller design, driver assistance systems, etc. Although simulation environments, measurement tools and mathematical theories on vehicle dynamics are well established, the methodical link between the experimental test data and validity analysis of the simulation model is still lacking. This report presents different views on the definition of validation, and its usage in vehicle dynamics simulation models.     

Development of a path planning system using mean shift algorithm for driver assistance

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

A new shared control for lane keeping and road departure prevention

In recent years, the driver's active assistances have become important features in commercialised vehicles. In this paper, we present one of these features which consists of an advanced driver assistance system for lane keeping. A thorough analysis of its performance and stability with respect to variations in driver behaviour will be given. Firstly, the lateral control model based on visual preview is established and the kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, and according to the characteristics of the lateral control, an efficient strategy of intelligent electric vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. A new fuzzy logic-based switching strategy with an efficient control law is also proposed to ensure a level of continuous and variable sharing according to the state of the driver and the vehicle positioning on the roadway. The proposed control law acts either at the centre of the lane, as a lane keeping assistance system to reduce the driver's workload for long trips, or as a lane departure avoidance system that intervenes for unintended lane departures. Simulation results are included in this paper to explain this concept.     

基于CFD的双道超车过程气动特性分析

基于计算流体力学（CFD）中的动网格技术对双道超车进行了数值模拟，研究表明，双道超车过程中，3辆车周围均出现强烈的压强和空气流速变化，当主超车在两车之间时，流场变化更为复杂。随着车辆相对位置的变化，3辆车周围的涡形态发生明显变化，涡的变化消耗流场中的能量，使车身受力发生变化。行驶过程中，3辆车的侧向力、风压中心位置和横摆力矩均发生明显变化。绝对速度增加，主超车与被超车的最大侧向力均呈线性增长，给车辆行驶稳定性带来负面影响。     

An adaptive lateral preview driver model

Successful modelling and simulation of driver behaviour is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In other words, this model template can be used in the design process of vehicles and active safety systems to assess their performance under average drivers as well as atypical drivers. We assume that the drivers, regardless of their style, have driven the vehicle long enough to establish an accurate internal model of the vehicle. The proposed driver model is developed using the adaptive predictive control (APC) framework. Three key features are included in the APC framework: use of preview information, internal model identification and weight adjustment to simulate different driving styles. The driver uses predicted vehicle information in a future window to determine the optimal steering action. A tunable parameter is defined to assign relative importance of lateral displacement and yaw error in the cost function to be optimized. The model is tuned to fit three representative drivers obtained from driving simulator data taken from 22 human drivers.     

In and out vision-based driver-interactive assistance system

The overall driving environment consists of the Traffic environment, vehicle and driver states (TVD). advanced driver assistance Systems (ADAS) must consider not only information on each of the TVD states but also their context. Recent research has focused on making more efficient and effective assistance systems by fusing all the information from the TVD states. Based on this research trend, this paper focuses on decision-level fusion to estimate the level of danger of a warning by using visual information of the traffic environment and the driver state. The driver state consists of the gazing region and the facial feature points that are obtained using the active appearance model (AAM). The traffic environment state consists of time to collision (TTC), time to lane Crossing (TLC), and lane color information, which are obtained from the environment in front of the vehicle, i.e., position of lanes and other vehicles. Warnings against lane-off, collision, and driver inattention are generated by fusing this vision-based information from inside and outside the vehicle. The experimental results prove that our vision-based interactive driver assistance system reduces most useless warnings.     

Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations

This paper presents a new method for the design and validation of advanced driver assistance systems (ADASs). With vehicle hardware-in-the-loop (VEHIL) simulations, the development process, and more specifically the validation phase, of intelligent vehicles is carried out safer, cheaper, and is more manageable. In the VEHIL laboratory, a full-scale ADAS-equipped vehicle is set up in a hardware-in-the-loop simulation environment, where a chassis dynamometer is used to emulate the road interaction and robot vehicles to represent other traffic. In this controlled environment, the performance and dependability of an ADAS is tested to great accuracy and reliability. The working principle and the added value of VEHIL are demonstrated with test results of an adaptive cruise control and a forward collision warning system. On the basis of the ‘V’ diagram, the position of VEHIL in the development process of ADASs is illustrated.     

Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations

This paper presents a new method for the design and validation of advanced driver assistance systems (ADASs). With vehicle hardware-in-the-loop (VEHIL) simulations, the development process, and more specifically the validation phase, of intelligent vehicles is carried out safer, cheaper, and is more manageable. In the VEHIL laboratory, a full-scale ADAS-equipped vehicle is set up in a hardware-in-the-loop simulation environment, where a chassis dynamometer is used to emulate the road interaction and robot vehicles to represent other traffic. In this controlled environment, the performance and dependability of an ADAS is tested to great accuracy and reliability. The working principle and the added value of VEHIL are demonstrated with test results of an adaptive cruise control and a forward collision warning system. On the basis of the 'V' diagram, the position of VEHIL in the development process of ADASs is illustrated.     

A passenger car driver model for higher lateral accelerations

Due to increasing demands for time and cost efficient vehicle and driver assistant systems development, numerical simulation of closed-loop manoeuvres becomes increasingly important. Thus, the driver has to be considered in the modelling. On the basis of a two-layer approach to model a driver's steering behaviour, the field of application is extended to higher lateral accelerations in this study. An analytical method to determine the driver parameters is presented, which is based on the two-wheel vehicle model. The simulation results are determined using a full vehicle model including all essential nonlinearities. Standard manoeuvres in the nonlinear range of vehicle handling behaviour are performed. A cornering manoeuvre is chosen to show the characteristics of the proposed driver model.     

A passenger car driver model for higher lateral accelerations

Due to increasing demands for time and cost efficient vehicle and driver assistant systems development, numerical simulation of closed-loop manoeuvres becomes increasingly important. Thus, the driver has to be considered in the modelling. On the basis of a two-layer approach to model a driver's steering behaviour, the field of application is extended to higher lateral accelerations in this study. An analytical method to determine the driver parameters is presented, which is based on the two-wheel vehicle model. The simulation results are determined using a full vehicle model including all essential nonlinearities. Standard manoeuvres in the nonlinear range of vehicle handling behaviour are performed. A cornering manoeuvre is chosen to show the characteristics of the proposed driver model.     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

The characteristic velocity stability indicator for passenger cars

Driver assistance systems have received increased attention as market demands have pushed for improved automotive safety. These systems are designed to aid the driver by preventing any unstable or unpredictable vehicle behaviour. One global indicator for stability and driving conditions could help to manage the control algorithms and driver warning subroutines. Another problem which could be solved by a precise driving situation indicator is evaluating new vehicles during test drives. After a short introduction to a linear lateral vehicle model, an analytical approach for an online calculation of different driving conditions (i.e., stability, understeering, oversteering, and neutralsteering) is given. A characteristic velocity stability indicator is defined, which allows online computation of the present driving condition. Results are then checked against real measurements of a test vehicle.     

The characteristic velocity stability indicator for passenger cars

Driver assistance systems have received increased attention as market demands have pushed for improved automotive safety. These systems are designed to aid the driver by preventing any unstable or unpredictable vehicle behaviour. One global indicator for stability and driving conditions could help to manage the control algorithms and driver warning subroutines. Another problem which could be solved by a precise driving situation indicator is evaluating new vehicles during test drives. After a short introduction to a linear lateral vehicle model, an analytical approach for an online calculation of different driving conditions (i.e., stability, understeering, oversteering, and neutralsteering) is given. A characteristic velocity stability indicator is defined, which allows online computation of the present driving condition. Results are then checked against real measurements of a test vehicle.     

Vehicle detection system design based on stereo vision sensors

Vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, a vehicle detection system using stereo vision sensors is developed. This system utilizes feature extraction, epipoplar constraint and feature matching in order to robustly detect the initial corresponding pairs. The proposed system can detect a leading vehicle in front and can estimate its position parameters such as the distance and heading angle. After the initial detection, the system executes the tracking algorithm for the vehicles in the lane. The proposed vehicle detection system is implemented on a passenger car and its performances are verified experimentally.     

基于注意力Seq2Seq网络的高速公路交织区车辆变道轨迹预测

针对交通状态复杂的高速公路交织区域，经验丰富的驾驶人能够通过正确地推断周围车辆的未来运动进行及时的车道变换，这对于实现安全高效的自动驾驶至关重要，然而目前的自动驾驶车辆往往缺乏这种预测能力。为此，基于深度学习理论，提出了一种结合注意力机制和编-解码器结构的交织区车辆强制性变道轨迹预测方法，利用Next Generation Simulation（NGSIM）数据集提取车辆变道过程中的关键特征，并引入碰撞时间（Time to Collision，TTC）和避免碰撞减速度（Deceleration Rate to Avoid a Crash，DRAC）2种风险指标，将变道车辆及其周围车辆视为一个整体状态单元，同时补全状态单元内部不同车辆在横向和纵向上的时空状态特征，从而更有效地刻画车辆间的动态交互行为；然后将不同观测车辆的连续窗口序列输入基于长短期记忆网络（Long Short-term Memory，LSTM）的编-解码器，预测交织区车辆变道的未来运动轨迹，通过添加软注意力模块，使模型能够集中聚焦于影响车辆在不同时刻下位置变化的关键信息，再现了真实交通场景下车辆的变道行为。试验验证表明：基于注意力机制的编-解码器模型与当前流行的卷积长短期记忆网络、极限梯度提升树等模型相比具有更高的轨迹预测精度，在长时域的变道轨迹拟合上有显著的优越性，为辅助和自动驾驶领域的发展提供了新思路。     

A closed-loop dynamic simulation-based design method for articulated heavy vehicles with active trailer steering systems

This paper presents a closed-loop dynamic simulation-based design method for articulated heavy vehicles (AHVs) with active trailer steering (ATS) systems. AHVs have poor manoeuvrability at low speeds and exhibit low lateral stability at high speeds. From the design point of view, there exists a trade-off relationship between AHVs’ manoeuvrability and stability. For example, fewer articulation points and longer wheelbases will improve high-speed lateral stability, but they will degrade low-speed manoeuvrability. To tackle this conflicting design problem, a systematic method is proposed for the design of AHVs with ATS systems. In order to evaluate vehicle performance measures under a well-defined testing manoeuvre, a driver model is introduced and it ‘drivers’ the vehicle model to follow a prescribed route at a given speed. Considering the interactions between the mechanical trailer and the ATS system, the proposed design method simultaneously optimises the active design variables of the controllers and passive design variables of the trailer in a single design loop (SDL). Through the design optimisation of an ATS system for an AHV with a truck and a drawbar trailer combination, this SDL method is compared against a published two design loop method. The benchmark investigation shows that the former can determine better trade-off design solutions than those derived by the latter. This SDL method provides an effective approach to automatically implement the design synthesis of AHVs with ATS systems.     

Lateral Collision Avoidance Robust Control of Electric Vehicles Combining a Lane-Changing Model Based on Vehicle Edge Turning Trajectory and a Vehicle Semi-Uncertainty Dynamic Model

This paper presents a new control scheme for lateral collision avoidance (CA) systems to improve the safety of four-in-wheel-motor-driven electric vehicles (FIWMD-EVs). There are two major contributions in the design of lateral CA systems. The first contribution is a new lane-changing model based on vehicle edge turning trajectory (VETT) to make vehicle adapt to different driving roads and conform to drivers’ characteristic, in addition to ensure vehicle steering safety. The second contribution is vehicle semi-uncertainty dynamic model (SUDM), which is SISO model. The problem of stability performance without the information on sideslip angle is solved by the proposed SUDM. Based on the proposed VETT and SUDM, the lateral CA system can be designed with H_∞ robust controller to restrain the effect of uncertainties resulting from parameter perturbation and lateral wind disturbance. Single and mixed driving cycles simulation experiments are carried out with CarSim to demonstrate the effectiveness in control scheme, simplicity in structure for lateral CA system based on the proposed VETT and SUDM.