Cornelia Lex

Drivers’ Interaction with Adaptive Cruise Control on Dry and Snowy Roads with Various Tire-Road Grip Potentials

This study investigates drivers’ interaction with Adaptive Cruise Control (ACC) in different road conditions and identifies areas of improvement. Ninety-six drivers drove with the ACC in a driving simulator showing either a summer scenery and a dry road with high grip potential or a winter scenery with a snowy road and reduced grip potential. The results show that on snowy roads the drivers set in average a lower ACC speed and preferred a larger ACC time gap. Drivers’ workload and effort were higher when using the ACC on snowy as compared to dry roads. Generally, the use of a shorter ACC gap resulted in lower ratings of comfort, safety, and trust and higher ratings of mental workload and effort in both dry and snowy road conditions. The drivers judged that ACC was braking too late and maintained a too short gap to the forward vehicle, especially when the ACC was set to 1 second as compared to a 1.8-second time gap. A future adaptation of ACC’s control strategy to reduced tire-road grip potential would not only improve comfort and user acceptance of the human driver but also increase the potential to react in emergency situations with braking or evasive steering.

Road friction estimation using Recursive Total Least Squares

Automated vehicles require information on the current road condition, i.e. the tire-road friction coefficient (μ_max) for trajectory planning and braking or steering interventions. Recursive Total Least Squares (RTLS) is used to estimate μ_max only utilizing the information from Electric Power System (EPS) and other sensors installed in production vehicles. A new state α_f/μ_max (front wheel slip angle divided by μ_max) is introduced which is observed by a proposed nonlinear observer. This state serves as a measurement for friction estimation and judge when the estimation result is reliable. The proposed method is verified in IPG CarMaker.

Experimental validation of the Maxwell model for description of transient tyre forces

Modelling and simulation of safety relevant Driver Assistance Systems (DAS) and Vehicle Dynamics Controllers (VDC) which act in standard and limit situations lead to increasing accuracy demands in the description of dynamic reactions of tyre contact forces, e.g. [4], [7]. For that purpose, first-order approaches are widely applied in this field of vehicle dynamics and handling, which originate from Schlippe & Dietrich [13], were modified by Pacejka [10] and later on refined by Rill [11], [12].

Evaluation of the potential of active powertrain, braking and steering systems based on in-wheel motors to improve the effectiveness of an evasive manoeuvre assistant

When different vehicle dynamic control systems are integrated into an overall concept, the potential to influence the vehicle dynamics rises, thus increasing the potential of performance in safety-critical driving situations. The potential of active steering, powertrain and braking systems and their combinations is investigated systematically in order to increase the efficiency of an evasive manoeuvre assistant. Different powertrain topologies are taken into account. A vehicle dynamics control is presented that determines the actuator control signals for different actuator configurations. An algorithm to evaluate the maximum coefficient of friction serves as the basis to decide on how the actuator signals are distributed to the individual wheels. This study supports selecting promising concepts in the broad variety of possible combinations of available systems.

A Car2X sensor model for virtual development of automated driving

Automated driving requires a reliable digital representation of the environment, which is achieved by various vehicle sensors. Wireless devices for communication between vehicles and infrastructure (Car2X communication) provide additional data beyond the vehicle’s sensor range. In order to reduce the amount of on-road testing, there has been an increased use of numerical simulation in the development of automated driving functions, which demands accurate simulation models for the sensors involved. The present research deals with the development of Car2X sensor models for conceptual, automated driving investigations based on relatively simple yet computationally efficient mathematical models featuring parameters derived from on-road hardware testing. For analysis purposes, variations in range and reliability in different driving situations were measured and depicted in Google Earth. For the sensor model, a combination of geometric and stochastic models was chosen. The modeling is based on a link budget calculation that considers system and path losses, where wave propagation is described using Nakagami probability density functions. For intersections, an additional term is added to account for the path loss with geometric parameters of the intersection. After model parametrization, an evaluation was conducted. In addition, as a sample case, Car2X was added to an adaptive cruise control, and the improved functionality was demonstrated using vehicle dynamics simulation. This extended adaptive cruise control used information from the indicator of surrounding vehicles to react faster to lane changes by these vehicles.

Nonlinear adaptive observer for side slip angle and road friction estimation

The side slip angle of a vehicle as well as the tire-road friction coefficient are important inputs for vehicle dynamics control system and automated driving modules. However measurement of these parameters are difficult and costly in mass production vehicles and need to be reliably and accurately estimated. We address the observer design problem for simultaneously estimating side slip angle and tire-road friction utilizing information from vehicle Electric Power Steering System (EPS). A key observation is that the vehicle dynamics can be transformed into a lower-triangular form. For non-affine parametrized systems in such a form we propose a nonlinear adaptive observer and prove the uniform exponential stability of the estimation error by constructing a strict Lyapunov function. The design procedure is subsequently applied to the vehicle observer design problem. Simulations demonstrate the robustness of the proposed observer against modeling error and measurement noise.

Tyre Dynamics: Model Validation and Parameter Identification

The present paper deals with the experimental validation of tyre dynamics approaches as it is widely applied in tyre models for vehicle dynamics and handling. Firstly it gives a brief derivation of two modelling principles regarding the deflection velocity in the considered direction of the tyre’s deformation. This is than followed by a brief description of the performed measurement procedure. From the measurements, a set of model parameters of the considered tyre, depending on different manoeuvre speeds and frequencies, is identified, where no particular fitting parameters for the tyre dynamics are needed. Based on these model parameters, the related dynamic simulations are carried out. The comparisons show that the applied first-order model describes the behaviour quite well within a certain operation range, whereas the second-order approach cannot deliver better results in spite of the longer computational time. However, for investigations within an enlarged frequency range of the steer input and at high slip angles, a more detailed model is recommended.

Robust road friction estimation during vehicle steering

ABSTRACT Automated vehicles require information on the current road condition, i.e. the tyre–road friction coefficient for trajectory planning, braking or steering interventions. In this work, we propose a framework to estimate the road friction coefficient with stability and robustness guarantee using total aligning torque in vehicle front axle during steering. We first adopt a novel strategy to estimate the front axle lateral force which performs better than the classical unknown input observer. Then, combined with an indirect measurement based on estimated total aligning torque and front axle lateral force, a non-linear adaptive observer is designed to estimate road friction coefficient with stability guarantee. To increase the robustness of the estimation result, criteria are proposed to decide when to update the estimated road conditions. Simulations and experiments under various road conditions validate the proposed framework and demonstrate its advantage in stability by comparing it with the method utilising the wide-spread Extended Kalman Filter.

Experimental validation of different approaches for thermodynamic simulation of passenger car tyres

Due to the intensified integration of simulation and modelling into the development of automotive vehicles and their assistance systems, the expectations of accuracy and computational efficiency in simulation are increasing rapidly. In addition, simulation finds more and more application in the process of vehicle homologation, which were a pure experimental discipline in the past. In any case, not only demands affecting vehicle modelling but also refined tyre modelling become more important. In recent years, with continuing refinement in tyre simulation, the needs for coping with tyre temperatures and the resulting influences on the tyre characteristics have been considered important.

Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

A recursive least squares (RLS) based observer for simultaneous estimation of vehicle mass and road grade, using longitudinal vehicle dynamics, is presented. In order to achieve robustness to unknown disturbances and varying parameters, depth is chosen in a sufficient way. This is done with a sensitivity analysis, identifying parameters with significant influence on the estimation result. The identification of vehicle parameters is presented in detail. The method is validated with an all-electric vehicle (AEV) using natural driving cycles. The results show little deviation between estimation and reference, as well as good convergence in urban areas, providing sufficient excitation. However, on highway roads, environmental influences like wind and slipstream of trucks, worsen the results, especially in combination with little excitation for the observer.