Driver-Vehicle-Lateraldynamics under Regular Driving Conditions

SUMMARY

In order to minimize accidents and achieve comfortable handling it is necessary to analyse the control behaviour of the driver-vehicle system and adapt the vehicle to the driver. However, most of the tests have shown, that under normal driving conditions the driver adapts himself to the vehicle, which is exactly the other way round as it was originally thought. In this paper it would be shown, how the driver adapts himself to the vehicle and which technical parameters may effect this adaptation.     

爱卡车,爱生活

来自河南驻马店的侯师傅今年34岁,本来可以成为大学生的他,却因为巨大的家庭负担,成为了一名风里来雨里去的卡车驾驶员。一聊起他的卡车生活,就像打翻了的五味瓶,酸甜苦辣,什么味道都有。     

Route adaptation of control strategies for a hybrid city bus

A series hybrid city bus with diesel engine and electric batteries is studied on a specified route. The study uses two different basic control strategies, “On/off” and “Continuous” strategy. These basic strategies are complemented in two ways. First, an “Adviser” strategy which filters the driver commands and gives driver support feedback based on the route data. Second, an “Adapter” strategy, which adapts and the control to the route, using different control depending on the vehicle present position. Simulation results are presented. They show that the adviser and adapter strategies improves both emissions and fuel consumption.     

ISG hybrid powertrain: a rule-based driver model incorporating look-ahead information

According to European regulations, if the amount of regenerative braking is determined by the travel of the brake pedal, more stringent standards must be applied, otherwise it may adversely affect the existing vehicle safety system. The use of engine or vehicle speed to derive regenerative braking is one way to avoid strict design standards, but this introduces discontinuity in powertrain torque when the driver releases the acceleration pedal or applies the brake pedal. This is shown to cause oscillations in the pedal input and powertrain torque when a conventional driver model is adopted. Look-ahead information, together with other predicted vehicle states, are adopted to control the vehicle speed, in particular, during deceleration, and to improve the driver model so that oscillations can be avoided. The improved driver model makes analysis and validation of the control strategy for an integrated starter generator (ISG) hybrid powertrain possible.     

Evaluation of preview G-Vectoring control to decelerate a vehicle prior to entry into a curve

This paper describes the development of the braking assistance system based on a “G-Vectoring” concept. The present work focuses in particular on “Preview G-Vectoring Control” (PGVC), which is based on the “G-Vectoring Control” (GVC) scheme. In GVC, the longitudinal-acceleration control algorithm is based on the actual lateral jerk. PGVC decelerates a vehicle before it enters a curve, and is based on a new longitudinal-acceleration control algorithm which uses predicted and actual lateral jerk. Using the predicted lateral jerk makes it possible to decelerate the vehicle prior to curve entry. This deceleration can emulate a driver’s deceleration as the vehicle approaches a curve entry. PGVC is based on such deceleration algorithms and enables automatic deceleration similar to the action of a driver. It is thus possible to significantly improve the driver’s feeling when this system is activated. Driving tests with this new control system on snowy-winding course confirmed that the automatic brake control quality improved considerably compared to manual driver control considering both lap time and ride quality. These results indicate that PGVC can be a useful braking assistance system not only to improve the driver’s handling performance but also to reduce the brake-task during driving on winding roads.     

From robotics to automotive: Lane-keeping and collision avoidance based on elastic bands

In the near future, drivers will more and more share vehicle guidance with assistance systems. This contribution provides a potential field-based approach to the underlying motion planning problem. In doing so, the concept of elastic bands, known from robotics, is extended to automotive applications. Contrary to robotic applications, extrapolation routines anticipating the motion of the surrounding traffic are incorporated in the motion planning. New in this paper is the distinction of different types of obstacles such as traffic staying in its lane and traffic intending to depart from it. Beyond that, the motion planning adapts to the driver's commands. The driver can be included in the overall control loop by means of a haptic interface generating a torque that depends on the difference of the actual steering angle and the steering angle necessary to follow the planned trajectory. However, this contribution focuses only on the underlying motion planning procedure.     

Minimizing the Path Radius of Curvature for Collision Avoidance

When a driver is suddenly presented with an obstacle in his path, or realizes that his speed is too great for the curved road ahead, commonly he saturates both inputs of steering and braking and thereby jeopardizes his chances of successfully avoiding a collision or negotiating the turn. Although anti-lock braking systems (ABS) avoid saturation of the braking and steerability usually remains, there is evidence to suggest that the vehicle performance with this system could be greatly improved. Could the steering, in addition to the braking, be automatically controlled to improve the performance? Because these threatening situations are so variable, it is very difficult to find a controller which can override both driver inputs and is always beneficial. Using a very simple model of the vehicle, the concept of minimizing the average radius of curvature of the path through controlling both driver inputs is shown to always be beneficial, and worthwhile. The results also carry over to a more realistic model.     

A Research on Analytical Method of Driver-Vehicle-Environment System for Construction of Intelligent Driver Support System

In this study, a driver model with the multiple regression and the neural network is constructed to analyze the relationship between the driver's control action and the information that includes data of vehicle behavior and environment. Using these models, effectiveness of the information to control the action of a driver is examined. To evaluate the intelligent driver support systems, Mental Work Load (MWL) model is constructed with the multiple regression and the neural network. MWL is expressed as Heart Rate Variability (HRV). Measured HRV data and calculated HRV data with MWL model show good agreement. Effectiveness of information is examined using the MWL model. From these results, it is shown that the analytical method with the driver's MWL can be used to assess and improve the intelligent driver support systems as the next stage of this research.     

Steady Turning of Two-Wheeled Vehicles

When driving along a circular path, the driver of a motorcycle controls the vehicle mainly by means of steering torque. If low steering torque is necessary, the driver feels that the vehicle is manoeuvrable. In this paper, a mathematical model concerning steering torque is developed; it takes into account the actual kinematic behaviour of the vehicle and the properties of motorcycle tyres. Tyre forces act at the contact points of toroidal tyres, which are calculated according to kinematic analysis. Non-linear equations are solved using an iterative approach. Several numerical results are presented, and the influence of tyre properties and some geometrical and inertial properties of the vehicle on steering torque are discussed.     

Novel stability control strategy for distributed drive electric vehicle based on driver operation intention

In this paper, a novel direct yaw control method based on driver operation intention for stability control of a distributed drive electric vehicle is proposed. It was discovered that the vehicle loses its stability easily under an emergency steering alignment (EA) problem. An emergent control algorithm is proposed to improve vehicle stability under such a condition. A driver operation intention recognition module is developed to identify the driving conditions. When the vehicle enters into an EA condition, the module can quickly identify it and transfer the control method from normal direct yaw control to emergency control. Two control algorithms are designed. The emergency control algorithm is applied to an EA condition while the adaptive control algorithm is applied to other conditions except the EA condition. Both simulation results and real vehicle results show that: The driver module can accurately identify driving conditions based on driver operation intention. When the vehicle enters into EA condition, the emergent control algorithm can intervene quickly, and it has proven to outperform normal direct yaw control for better stabilization of vehicles.     

Steady Turning of Two-Wheeled Vehicles

When driving along a circular path, the driver of a motorcycle controls the vehicle mainly by means of steering torque. If low steering torque is necessary, the driver feels that the vehicle is manoeuvrable. In this paper, a mathematical model concerning steering torque is developed; it takes into account the actual kinematic behaviour of the vehicle and the properties of motorcycle tyres. Tyre forces act at the contact points of toroidal tyres, which are calculated according to kinematic analysis. Non-linear equations are solved using an iterative approach. Several numerical results are presented, and the influence of tyre properties and some geometrical and inertial properties of the vehicle on steering torque are discussed.     

A Research on Analytical Method of Driver-Vehicle-Environment System for Construction of Intelligent Driver Support System

In this study, a driver model with the multiple regression and the neural network is constructed to analyze the relationship between the driver's control action and the information that includes data of vehicle behavior and environment. Using these models, effectiveness of the information to control the action of a driver is examined. To evaluate the intelligent driver support systems, Mental Work Load (MWL) model is constructed with the multiple regression and the neural network. MWL is expressed as Heart Rate Variability (HRV). Measured HRV data and calculated HRV data with MWL model show good agreement. Effectiveness of information is examined using the MWL model. From these results, it is shown that the analytical method with the driver's MWL can be used to assess and improve the intelligent driver support systems as the next stage of this research.     

Time-optimal control of the race car: a numerical method to emulate the ideal driver

A numerical method for the time-optimal control of the race car is presented. The method is then used to perform the role of the driver in numerical simulations of manoeuvres at the limit of race car performance. The method does not attempt to model the driver but rather replaces the driver with methods normally associated with numerical optimal control. The method simultaneously finds the optimal driven line and the driver control inputs (steer, throttle and brake) to drive this line in minimum time. In principle, the method is capable of operation with arbitrarily complex vehicle models as it requires only limited access to the vehicle model state vector. It also requires solution of the differential equation representing the vehicle model in only the forward time direction and is hence capable of simulating the full vehicle transient response.     

A lateral driver model for vehicle–driver closed-loop simulation at the limits of handling

This paper presents a lateral driver model for vehicle–driver closed-loop simulation at the limits of handling. An appropriate driver model can be used to evaluate the performance of vehicle chassis control systems via computer simulations before vehicle tests which incurs expenses especially at the limits of handling. The driver model consists of two parts. The first part is an upper-level controller employing force-based approach to reduce the number of unknown vehicle parameters. The feedforward part of the upper controller has been designed by using the centre of percussion. The feedback part aims to minimise ‘tangential error’, defined as the sum of body slip angle and yaw error, to match vehicle direction and road heading angle. The part is designed to regenerate an appropriate skid motion similar to that of a professional driver at the limits. The second part is a lower-level controller which converts the desired front lateral force to steering wheel angle. The lower-level controller also consists of feedforward and feedback parts. A two-degree-of-freedom bicycle model-based feedforward part provides nominal steering wheel angle, and the feedback part aims to eliminate unmodelled error. The performance of the lateral driver model has been investigated via computer simulations. It has been shown that the steering behaviours of the proposed driver model are quite close to those of a professional driver at the limits. Compared with the previously developed lateral driver models, the proposed lateral driver model shows good tracking performance at the limits of handling.     

Driver propensity characterization for different forward collision warning times

The forward collision warning system, which warns danger to the driver after sensing possibility of crash in advance, has been actively studied recently. Such systems developed until now give a warning, regardless of driver’s driving propensity. However, it’s not reasonable to give a warning to every driver at the same time because drivers are different in driving propensity. In this study, to give a warning to each driver differently, three metrics classifying driver’s driving propensity were developed by using the driving data on a testing ground. These three metrics are the predicted time headway, required deceleration divided by the deceleration of the leading vehicle, and the resultant acceleration divided by the deceleration of the leading vehicle. Driving propensity was divided into 3 groups by using these metrics for braking and steering cases. In addition, these metrics were verified by making sure that braking propensity could be classified on public roads as well.     

Active Dual Mode Tilt Control for Narrow Ground Vehicles

Small, narrow commuter vehicles have attracted considerable interest in recent years as a means to increase the utilization of existing freeways and parking facilities. However, conventional narrow track vehicles are likely to have reduced stability against overturning during hard cornering. A possible solution to this problem lies in vehicles which tilt toward the inside of a turn. Two different ways to achieve this tilt will be analyzed. For direct tilt control (DTC) an actuator forces the upper part of the vehicle to tilt. Steering tilt control (STC) uses steering to control the tilt as is done by motorcycle or bicycle riders. At low speeds, only the DTC system is effective while at high speeds the STC offers less lateral acceleration for the passenger during transient cornering and may seem more natural. The two methods of control will be studied separately and it will be shown that even though the same steady state tilt can be achieved with either system, the transient behavior of the systems is very different. It also will be shown that it is possible to switch from one system to the other at an arbitrarily chosen speed with virtually no transient effects even when the vehicle is not in a steady state. Regardless of which control system is active, the driver simply communicates his desire to follow the road by moving the steering wheel and the control systems take care of the tilting either by using the tilt actuator or by actively steering the road wheels. Thus the driver does not need to leam how to stabilize the tilt mode of the vehicle.     

Application of Elementary Neural Networks and Preview Sensors for Representing Driver Steering Control Behaviour

This paper demonstrates the use of elementary neural networks for modelling and representing driver steering behaviour in path regulation control tasks. Areas of application include uses by vehicle simulation experts who need to model and represent specific instances of driver steering control behaviour, potential on-board vehicle technologies aimed at representing and tracking driver steering control behaviour over time, and use by human factors specialists interested in representing or classifying specific families of driver steering behaviour. Example applications are shown for data obtained from a driver/vehicle numerical simulation, a basic driving simulator, and an experimental on-road test vehicle equipped with a camera and sensor processing system.     

eTS fuzzy driver model for simultaneous longitudinal and lateral vehicle control

In this paper, evolving Takagi-Sugeno (eTS) fuzzy driver model is proposed for simultaneous lateral and longitudinal control of a vehicle in a test track closed to traffic. The developed eTS fuzzy driver model can capture human operator’s driving expertise for generating desired steering angle, throttle angle and brake pedal command values by processing only information which can be supplied by the vehicle’s on-board control systems in real time. Apart from other fuzzy rule based (FRB) models requiring human expert knowledge or off-line clustering, the developed eTS driver model can adapt itself automatically, even ‘from scratch’, by an on-line learning process using eTS algorithm while human driver is supervising the vehicle. Proposed eTS fuzzy driver model’s on-line human driver identification capability and autonomous vehicle driving performance were evaluated on real road profiles created by digitizing two different intercity express ways of Turkey in IPG© CarMaker® software. The training and validation simulation results demonstrated that eTS fuzzy driver model can be used in product development phase to speed up different tests via realistic simulations. Furthermore eTS fuzzy driver model has an application potential in the field of autonomous driving.     

汽车驾驶安全隐患预防与对策探索

司机是一个比较特殊的工作,通常在驾驶汽车时,司机要确保整个驾驶过程的安全,这就要求司机要具备较高的驾驶素质。如果驾驶员自身的安全意识不高,对车辆驾驶工作没有太深的安全意识,就会给行车工作带来很大的安全隐患。如果司机对驾驶工作安全性意识较高,则行车就会更加安全,司机也会提高自己的综合素质和驾驶技术来保证车辆行驶的安全,避免在行驶中出现一系列安全隐患。因此,在驾驶汽车时,要重点突出安全隐患的预防,并制定一系列的对策,才可能减少车辆的安全事故产生。     

Finite Disturbance Directional Stability of Vehicles with Human Pilot Considering Nonlinear Cornering Behavior

The driver of a vehicle has a significant influence on handling and stability of the vehicle. Due to the complex behavior of a human pilot, a driver model is usually neglected when dealing with the problem of vehicle stability. This work focuses on the interaction between the vehicle and the human pilot. A model characterizing human operator behavior in a regulation task is employed to study directional stability. Linear stability is analyzed by the application of the Routh-Hurwitz criterion and stability boundaries separating the stable domain of operation of the driver from the unstable one are constructed.

The linear analysis predicts that the only possible instability in a driver/vehicle system is an oscillatory instability with increasing amplitude. It is shown that the addition of kinematic as well as slip angle nonlinearities in the vehicle model can have a stabilizing effect on these oscillations of the combined driver/vehicle system. They may also be responsible for the opposite, namely a linearly stable motion may become unstable to finite size disturbances. These nonlinear motions are predicted by a bifurcation analysis and are verified by direct numerical simulation.