Arno Eichberger

Pressure measurements in the spinal canal of post-mortem human subjects during rear-end impact and correlation of results to the neck injury criterion.

The aim of this study is to validate the pressure effect theory on human beings during a realistic rear-end impact and to correlate the neck injury criterion to pressure in the spinal canal. Sled experiments were performed using a test setup similar to real rear-end collisions. Test conditions were chosen based on accident statistics and recordings of real accidents. In particular, velocity change and acceleration level were reproduced similar to actual collisions. The head restraint as well as the seat back were adjusted to different positions. Two small pressure transducer were implemented to the spinal canal of postmortem human subjects and pressure measurement similar to the pig experiments (using exactly the same equipment) were performed. A total set of 21 experiments with four different subjects were performed. The subjects were additionally instrumented with triaxial accelerometers that allowed for calculation of the NIC criterion. Results showed that NIC and pressure amplitudes of the CSF correlate well and therefore NIC seems to be able to predict these amplitudes also for human beings. Conclusions whether these pressure effects induce soft tissue neck injuries or not could not be drawn and should be investigated in further research.

Virtual Stochastic Testing of Advanced Driver Assistance Systems

With Advanced Driver Assistance Systems becoming increasingly complex, testing methods must keep up to efficiently test and validate these systems. This paper focuses on a method of testing vision-based Advanced Driver Assistance Systems on a state-of-the-art hardware-in-the-loop test bench. Virtual driving scenarios are being used for functional testing. This paper suggests a framework where the driving scenarios are constructed using a stochastical approach. This allows the testing of the parameter combinations that might otherwise be forgotten or disregarded by a human creating the scenarios. The first step of this framework, a road generator, is introduced. Generic courses of roads are created using the Markov Chain and Markov Chain Monte Carlo methods reconstructing real-life scenarios by analyzing map data.

Performance Evaluation of a Novel Vehicle Collision Avoidance Lane Change Algorithm

This study, proposes a methodology to evaluate the performance of a novel emergency lane change algorithm. The algorithm, defines a number of constraints, based on the vehicle’s dynamics and environmental conditions, which must be satisfied for a safe and comfortable lane change maneuver. Inclusion of the lateral position of other vehicles on the road, the tire-road friction, and real-time ability are the main advantages of the proposed algorithm. For performance evaluation of the developed algorithm, a set of driving scenarios were designed to consider different possible traffic situations that may appear in an emergency lane change maneuver. These scenarios were implemented later in IPG CarMaker, which is a vehicle’s dynamics platform. Based on the designed scenarios, the efficiency of the algorithm in collision free lane change maneuver was examined.

Data fusion to develop a driver drowsiness detection system with robustness to signal loss

This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.

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.

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.

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.