Thomas Brandmeier

Statistical Behavior Modeling for Driver-Adaptive Precrash Systems

Precrash systems have the potential for preventing or mitigating the results of an accident. However, optimal precrash activation can be only achieved by a driver-individual parameterization of the activation function. In this paper, an adaptation model is proposed, which calculates a driver-adapted activation threshold for the considered precrash algorithm. The model analyzes past situations to calculate a driver-individual activation threshold that achieves a desired activation frequency. The advantage of the proposed model is that the distribution is estimated using a distribution model. This has the result that an activation threshold can be already determined using a small data set. In addition, the confidence interval that has to be considered is decreased. The proposed model was applied in a study with test subjects. Results of this paper confirm the usability of the model. In comparison with an empirical approach, the proposed model achieves a significantly lower threshold and, thus, a higher safety effect of the system.

Criticality estimation of Pre-Crash scenarios

The activation of Pre-Crash systems like a reversible seat belt pretensioner can reduce the injury level of an occupant during a collision. The time-to-collision is a common criterion for discriminating between critical and non-critical situations. This paper investigates the drivers behavior as well as his/her criticality estimation of Pre-Crash scenarios and models the criticality estimation to a classification with features of the situation. Experimental data is analyzed regarding its possibility to model the subjective driver estimation by measured features. A situation model is developed that predicts a classification criterion, which can be used for Pre-Crash activation functions.

Test methodology for rain influence on automotive surround sensors

Automotive safety systems aim to provide maximum protection to vehicle occupants and vulnerable road users. These safety features rely on data from surround sensors such as radar, lidar and camera, which provide detailed information about the environment of the vehicle. A minor error in the measurements of these sensors can lead to major injuries or death. Hence, the reliability and accuracy of these sensor systems is mandatory. The performance of surround sensors depends on their local environment, because of the attenuation of the ambient atmosphere. Environmental influences such as rain additionally affects the accuracy of sensor systems. Therefore, these sensors must be tested under various weather conditions. This paper presents a new test methodology for rain influence on automotive surround sensors. Therefore, a rain simulator was designed and validated. The proposed test methodology was applied to radar, lidar and camera sensors in an experimental setup.

Modeling and simulation of rain for the test of automotive sensor systems

This paper presents a new approach for the test of automotive sensor systems in rain. The approach is based on an indoor test method, which helps to save test kilometers and test effort. For the activation of safety systems detailed information about the vehicles environment is necessary. Laser scanners provide precise information about the environment and a high angular resolution in contrast to radar sensors [1]. The performance of laser scanners depends on their local environment, because of the attenuation of the ambient atmosphere, precipitation and on the reflectivity of objects. False measurements in the field of vehicle safety can result in severe injury or death, so high reliability is essential. For this purpose a theoretical model is developed in order to determine the sensor behavior. Subsequently, a rain simulator is constructed to validate the theoretical model. Furthermore the developed rain simulator is validated by comparison with real rain. Based on determined rain disturbances benchmark tests of different sensor systems and algorithm approaches can be performed.

A Novel Approach for Researching Crossing Behavior and Risk Acceptance: The Pedestrian Simulator

To integrate unpredictable human behavior in the assessment of active and passive pedestrian safety systems, we introduce a virtual reality (VR)-based pedestrian simulation system. The device uses the Xsens Motion Capture platform and can be used without additional infrastructure. To show the systems applicability for pedestrian behavior studies, we conducted a pilot study evaluating the degree of realism such a system can achieve in a typical unregulated pedestrian crossing scenario. Six participants had to estimate vehicle speeds and distances in four scenarios with varying gaps between vehicles. First results indicate an acceptable level of realism so that the device can be used for further user studies addressing pedestrian behavior, pedestrian interaction with (automated) vehicles, risk assessment and investigation of the pre-crash phase without the risk of injuries.

A novel approach for intelligent pre-crash threat assessment systems

Compared to the state-of-the-art on integrated safety systems, earlier activated safety systems can further reduce the risk of suffering a major injury. Activation of such systems prior to a collision can be realized by analysing measurements of exteroceptive sensors (pre-crash data). An algorithm for estimating collisions in real-time using fused measurements of a video camera, a laser range finder (LRF), and ego vehicle motion sensors is presented. The threat posed by the actual driving situation is assessed by calculating a certain risk value, which is determined by combining the collision probability and crash severity estimations in a comprehensive way. A scale model vehicle is introduced to capture characteristics of the proposed system experimentally. First test runs show that the object width measurement is very accurate (absolute error of 5%) and the maximum time to collision (TTC) estimation error is around 17% about 300ms before the impact. Comparing different obstacles and impact scenarios (e.g. small overlap vs. full frontal collision), the calculated risk is a promising new measure to early discriminate crash types.

Advantages in Crash Severity Prediction Using Vehicle to Vehicle Communication

The paper discusses a new approach in contactless crash detection combining measurements of vehicle dynamics, exteroceptive sensors and vehicle-to-vehicle (V2V) communication data. The proposed architecture aims to activate vehicle safety functions prior an imminent collision to minimize the risk of suffering a major injury. An activation needs a precise prediction of time to collision (TTC), the crash severity (Cs) and other relevant crash parameters. This paper studies the contribution of V2V communication data to predict potential collisions and to realize a reliable activation. An algorithm is presented, that merges fused measurements of a video camera, a laser range finder (LRF) and ego vehicle motion sensors with V2V communication data to predict collisions. The benefit using V2V communication is demonstrated by evaluating collision prediction errors. This analysis is carried out based on experimental data produced by two scale model vehicles.

A Novel Approach for the Test of Active Pedestrian Safety Systems

Active pedestrian safety systems are based on a variety of sensor systems and detection algorithm approaches. The activation of, e.g., an emergency brake is a critical decision. Therefore, such applications must be tested very responsibly. This paper shows the characteristic features of pedestrians, presents current test methods, and introduces a novel test system approach and pedestrian dummy, which enables the test of advanced pedestrian detection systems. This includes also detection algorithm approaches including pre-indicators and path prediction for a complex motion pattern. The complex motion pattern can be a walking or a running pedestrian, including velocity and direction changes.

Context-aware system for pre-triggering irreversible vehicle safety actuators

New vehicle safety systems have led to a steady improvement of road safety and a reduction in the risk of suffering a major injury in vehicle accidents. A huge leap forward in the development of new vehicle safety systems are actuators that have to be activated irreversibly shortly before a collision in order to mitigate accident consequences. The triggering decision has to be based on measurements of exteroceptive sensors currently used in driver assistance systems. This paper focuses on developing a novel context-aware system designed to detect potential collisions and to trigger safety actuators even before an accident occurs. In this context, the analysis examines the information that can be collected from exteroceptive sensors (pre-crash data) to predict a certain collision and its severity to decide whether a triggering is entitled or not. A five-layer context-aware architecture is presented, that is able to collect contextual information about the vehicle environment and the actual driving state using different sensors, to perform reasoning about potential collisions, and to trigger safety functions upon that information. Accident analysis is used in a data model to represent uncertain knowledge and to perform reasoning. A simulation concept based on real accident data is introduced to evaluate the presented system concept.

Investigation of intelligent features for CFRP structure in automotive safety systems

New stringent European Union regulations, on both emission and fuel consumption are driving the automobile manufacturers in continuous search of suitable lightweight materials. Carbon Fiber Reinforced Plastic (CFRP) is one of the most prominent lightweight materials in use. In addition to the benefit of reduction in weight, the inherent properties of CFRP materials such as acoustic emission, electrical properties of fibers etc. change with the applied load during elastic deformation and failure. This change in properties is the base of the presented idea to assign intelligence to current automotive safety structures made from CFRP material. This paper gives a short review of the work done by various researchers in the field of health monitoring of CFRP structures using electrical resistance. Further, experiments were performed for initial investigations to show the promising usage of the above explained intelligence features. Tube-like and plate-like specimens were used, because of their similarity with automotive structures. It can be concluded, that the mechanisms like elastic bending and micro buckling have very negligible influence on resistance change compared to other mechanisms. The results from the experiments show significant resistance change within 5 to 50 milliseconds after impact. This demonstrates the potential of this concept to be used for crash severity detection in automobiles.