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This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

This paper presents a methodology of trajectory planning for the surrounding-aware lane change maneuver of autonomous vehicles based on a data-driven method. The lateral motion is planned by sampling candidate patterns which are defined based on quintic polynomial functions over time. Based on the cost evaluation among the sampled candidates, the optimal lateral motion pattern is selected as a reference and tracked by the controller. The longitudinal motion is planned and controlled using Model Predictive Control (MPC) which is an optimal control method designed considering the surrounding traffic information. To realize the lane change motion similar to the human driving behavior in the surrounding traffic situation, the human driving pattern is modeled in the form of motion parameters and considered in planning the lateral and longitudinal motion.

This paper presents an Interacting Multiple Model (IMM) based side slip angle estimation method to estimate side slip angle under various road conditions for vehicle stability control. Knowledge of the side slip angle is essential enhancing vehicle handling and stability. For the estimation of the side slip angles in previous researches, prior knowledge of tire parameters and road conditions have been employed, and sometimes additional sensors have been needed. These prior knowledge and additional sensors, however, necessitates many efforts and make an application of the estimation algorithm difficult. In this paper, side slip angle has been estimated using on-board vehicle sensors such as yaw rate and lateral acceleration sensors. The proposed estimation algorithm integrates the estimates from multiple Kalman filters based on the multiple models with different parameter set.

This paper proposes a rear-wheel steering control method that can modify and improve the vehicle lateral response without tire model and parameter. The proposed control algorithm is a combination of steady-state and transient control. The steady state control input is designed to modify steady-state yaw rate response of the vehicle, i.e. understeer gradient of the vehicle. The transient control input is a feedback control to improve the transient response when the vehicle lateral behavior builds up. The control algorithm has been investigated via computer simulations. Compared to classical control methods, the proposed algorithm shows good vehicle lateral response such as small overshoot and fast response. Specifically, the proposed algorithm can alleviate stair-shaped response of the lateral acceleration.

This paper presents an integrated chassis control method for vehicle stability under various road friction conditions without information on tire-road friction. For vehicle stability, vehicle with an integrated chassis control needs to cope with the various road friction conditions. One of the chassis control method under various road conditions is to determine and/or limit control inputs based on tire-road friction coefficient. The tire-road friction coefficient, however, is difficult to estimate and still a challenging task. The key idea for the proposed method without the estimation of the tire-road friction coefficient is to analyze and control vehicle states based on a tire slip angle - tire force phase plane, i.e. based on these vehicle responses: tire forces and tire slip angles of front/rear wheels.

This paper presents the integrated chassis control(ICC) of four-wheel drive(4WD), electronic stability control(ESC), electronic control suspension(ECS), and active roll stabilizer(ARS) for limit handling. The ICC consists of three layers: 1) a supervisor determines target vehicle states; 2) upper level controller calculates generalized forces; 3) lower level controller, which is contributed in this paper, optimally allocates the generalized force to chassis modules. The lower level controller consists of two integrated parts, 1) longitudinal force control part (4WD/ESC) and 2) vertical force control part (ECS/ARS). The principal concept of both algorithms is optimally utilizing the capability of the each tire by monitoring tire saturation, with tire combined slip. By monitoring tire saturation, 4WD/ESC integrated system minimizes the sum of the tire saturation, and ECS/ARS integrated system minimizes the variance of the tire saturation.

This paper presents an automated driving control algorithm for the control of an autonomous vehicle. In order to develop a highly automated driving control algorithm, one of the research issues is to determine a safe driving envelope with the consideration of probable risks. While human drivers maneuver the vehicle, they determine appropriate steering angle and acceleration based on the predictable trajectories of the surrounding vehicles. Therefore, not only current states of surrounding vehicles but also predictable behaviors of that should be considered in determining a safe driving envelope. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, the safe driving envelope over a finite prediction horizon is defined in consideration of probabilistic prediction of future positions of surrounding vehicles.

Typically, when one thinks of advanced driver assistance systems (ADAS), systems such as Forward Collision Warning (FCW) and Collision Imminent Braking (CIB) come to mind. In these systems driver assistance is provided based on knowledge about the subject vehicle and surrounding objects. A new class of these systems is being implemented. These systems not only use information on the surrounding objects but also use information on the driver's response to an event, to determine if intervention is necessary. As a result of this trend, an advanced level of understanding of driver braking behavior is necessary. This paper presents an alternate method of analyzing driver braking behavior. This method uses a frequency content based approach to study driver braking and allows for the extraction of significantly more data from driver profiles than traditionally would have been done.

In this research, a three degree-of-freedom (DOF) rack-type electric-based power steering (EPS) model is developed. The model is coupled with a three DOF vehicle model and includes EPS maps as well as non-linear attributes such as vibration and friction characteristics of the steering system. The model is simulated using Matlab's Simulink. The vibration levels are quantified using on-vehicle straight-line test data where strain-gauge transducers are placed in the tie-rod ends. Full vehicle kinematic and compliance tests are used to verify the total steering system stiffness levels. Frequency response tests are used to adjust tire cornering stiffness levels as well as the tire dynamic characteristics such that vehicle static gain and yaw natural frequency are achieved. On-center discrete sinusoidal on-vehicle tests are used to further validate the model.

In the view point of driving safety, the crosswind sensitivity of a vehicle becomes more important, as the driving speed in highway gets higher in these days. The sensitivity of a vehicle to crosswind depends on many factors, including the design of the suspension and aerodynamics of the body, etc. However, the knowledge about this phenomenon has still to be improved, in order to develop vehicle with optimum characteristics for crosswind stability. In this research, the physics behind the sensitivity of a vehicle is discussed in detail through various kinds of virtual test using computer aided engineering (CAE) simulation scheme. In the first, a reliable simulation model for vehicle, driver, wind generator and interactions among them is built. This simulation model is verified by comparison with test results of real vehicle. Then, the sensitivity analysis is carried out to find out the most influential design parameters.

Dynamic Brake Support (DBS) is a safety system that has been applied to various passenger cars and has been shown to be effective at assisting drivers in avoiding or mitigating rear-end collisions. The objective of a DBS system is to ensure that the brake system is applied quickly and at sufficient pressure when a driver responds to a collision imminent situation. DBS is capable of improving braking response due to a passenger car driver's tendency to utilize multi-stage braking. Interest is developing in using DBS on commercial vehicles. In order to evaluate the possible improvement in safety that could be realized through the use of DBS, driver braking behavior must first be analyzed to confirm that improvement is possible and necessary. To determine if this is the case, a study of the response of truck drivers' braking behavior in collision imminent situations is conducted. This paper presents the method of evaluation and results.

The (Vehicle Inertia Parameter Evaluation Rig) VIPER II is a full vehicle mass and inertia parameter measurement machine. The VIPER II expands upon the capabilities of its predecessor and is capable of measuring vehicles with a mass of up to 45,360 kg (100,000 lb), an increase in capacity of 18,100 kg (40,000 lb). The VIPER II also exceeds its predecessor in both the length and width of vehicles it can measure. The VIPER II's maximum vehicle width is 381 cm (150 in) an increase of 76 cm (30 in) and maximum distance from the vehicle CG to the outer most axle is 648 cm (255 in) an additional 152 cm (60 in) The VIPER II is capable of performing measurements including vehicle CG height, pitch, roll, and yaw moments of inertia and the roll/yaw cross product of inertia. While being able to measure both heavier and larger vehicles, the VIPER II is designed to maintain a maximum error of 3% for all inertia measurements and 1% for CG height.

A Software-in-the-Loop (SIL) simulation is presented here wherein control algorithms for the Anti-lock Braking System (ABS) and Roll Stability Control (RSC) system were developed in Simulink. Vehicle dynamics models of a 6×4 cab-over tractor and two trailer combinations were developed in TruckSim and were used for control system design. Model validation was performed by doing various dynamic maneuvers like J-Turn, double lane change, decreasing radius curve, high dynamic steer input and constant radius test with increasing speed and comparing the vehicle responses obtained from TruckSim against field test data. A commercial ESC ECU contains two modules: Roll Stability Control (RSC) and Yaw Stability Control (YSC). In this research, only the RSC has been modeled. The ABS system was developed based on the results obtained from a HIL setup that was developed as a part of this research.

This paper presents a methodology of correlation between subjective and objective measures of vehicle on-center handling performance. The subjective measure is a professional test driver's rating of vehicle handling, while the objective measure assesses the handling performance via vehicle dynamic responses. Vehicle test data obtained from field testing has been analyzed to investigate links between the objective and subjective measures. Fifty-six physical parameters have been derived from on-centering hysteresis curves. Statistical tools are employed to obtain good correlation between driver rating and physical parameters. Using an interaction formula, a statistical model which relates the driver rating and principal physical parameters has been obtained. The proposed methodology will be used to show the physical parameters influence on subjective assessment and even to predict the subjective assessment of a vehicle handling performance.

This paper presents a feedback linearization control technique as applied to a Roll Simulator. The purpose of the Roll Simulator is to reproduce in-field rollovers of ROVs and study occupant kinematics in a laboratory setting. For a system with known parameters, non-linear dynamics and trajectories, the feedback linearization algorithm cancels out the non-linearities such that the closed-loop dynamics behave in a linear fashion. The control inputs are computed values that are needed to attain certain desired motions. The computed values are a form of inverse dynamics or feed-forward calculation. With increasing system eigenvalue, the controller exhibits greater response time. This, however, puts a greater demand on the translational actuator. The controller also demonstrates that it is able to compensate for and reject a disturbance in force level.

This paper describes the mechanical design of a Suspension Parameter Identification Device and Evaluation Rig (SPIDER) for wheeled military vehicles. This is a facility used to measure quasi-static suspension and steering system properties as well as tire vertical static stiffness. The machine operates by holding the vehicle body nominally fixed while hydraulic cylinders move an “axle frame” in bounce or roll under each axle being tested. The axle frame holds wheel pads (representing the ground plane) for each wheel. Specific design considerations are presented on the wheel pads and the measurement system used to measure wheel center motion. The constraints on the axle frames are in the form of a simple mechanism that allows roll and bounce motion while constraining all other motions. An overview of the design is presented along with typical results.

This paper presents the design and evaluation of an emergency driving support (EDS) algorithm. The control objective is to assist driver's collision avoidance maneuver to overcome a hazardous situation. To support driver, electrically controllable chassis components such as motor driven power steering (MDPS) and differential braking and surrounding sensor systems such as radar and camera are used. The EDS algorithm is designed for 3 parts: monitoring, decision, and control. The proposed EDS algorithm recognizes a collision danger using minimum lateral acceleration to avoid collision and time-to-collision (TTC) and driver's intention using sensor systems. The control mode is determined using the indices from monitoring process and the collision avoidance trajectory is derived with trapezoidal acceleration profile (TAP).

This paper describes a driving motor torque distribution strategy of six-wheel-driven electric vehicles for optimized energy consumption. In this research, this strategy minimizes motoring power consumption and maximizes regenerative braking power under given required power condition. The torque distribution controller consists of total required motor torque calculation part, upper and optimal torque calculation part, lower level controller. The upper level controller determines total required torque of vehicle. And the torque is determined by acceleration pedal input of driver and vehicle velocity. The lower level controller calculates energy consumption in given condition and distributes motor torque to driving motor minimizing energy consumption. In distributing optimal motor torque, it is important to get accurate characteristics of driving motor and performance constraint.

This paper is concerned with the torque distribution problem including slip limitation and actuator fault tolerance to improve vehicle lateral stability and maneuverability of six-wheeled skid-steered vehicles. The torque distribution algorithm to distribute wheel torque to each wheel of a skid-steered vehicle consists of an upper level control layer, a lower level control layer and an estimation layer. The upper level control layer is designed to obtain longitudinal net force and desired yaw moment, while the lower level control layer determines distributed driving and braking torques to six wheels. The algorithm takes vehicle speed, slip ratio and tire load information from the estimation layer, as well as actuator fault information from each in-wheel motor controller unit.

According to NHTSA's 2011 Traffic Safety Facts [1], passenger vehicle occupant fatalities continued the strong decline that has been occurring recently. In 2011, there were 21,253 passenger vehicles fatalities compared to 22,273 in 2010, and that was a 4.6% decrease. However; large-truck occupant fatalities increased from 530 in 2010 to 635 in 2011, which is a 20% increase. This was a second consecutive year in which large truck fatalities have increased (9% increase from 2009 to 2010). There was also a 15% increase in large truck occupant injuries from 2010. Moreover, the fatal crashes involving large trucks increased by 1.9%, in contrast to other-vehicle-occupant fatalities that declined by 3.6% from 2010. The 2010 accident statistics NHTSA's report reveals that large trucks have a fatal accident involvement rate of 1.22 vehicles per 100 million vehicle miles traveled compared to 1.53 for light trucks and 1.18 for passenger cars.