2 Modelling of the Driver s Braking Behaviour for Assessment Methodology of Active Pedestrian Protection Systems

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1 2 Modelling of the Driver s Braking Behaviour for Assessment Methodology of Active Pedestrian Protection Systems Dipl.-Ing. Dominik Raudszus, Dr.-Ing. Adrian Zlocki, Univ.-Prof. Dr.-Ing. Lutz Eckstein, Institut für Kraftfahrzeuge (ika), RWTH Aachen University 2.1 Introduction In recent years an increasing number of driver assistance systems supporting the driver in avoiding pedestrian accidents by providing warnings and by intervening automatically have been introduced. Since such systems are currently not considered in consumer ratings, e.g. Euro NCAP, the European funded research project AsPeCSS (Assessment methodologies for forward looking integrated Pedestrian and further extension to Cyclists Safety Systems) aims at developing a test procedure in order to evaluate the benefit of systems for active pedestrian protection. Due to the complex interaction between driver, vehicle and environment, a comprehensive test methodology cannot be applied without taking the driver s behaviour into account. In this paper, a methodology for a systematic development of a driver reaction model shall be presented. After giving a brief introduction to the AsPeCSS project, the requirements to the driver model and the methodology used to develop a model that meets these requirements is described. Following, the particular steps are discussed in more detail, giving some first findings. 2.2 The AsPeCSS Project According to the World Health Organisation global status report on road safety 2009, pedestrians account for more than 19% of road fatalities in the EU-27. It is well known that most accidents with pedestrians are caused by the driver being unalert or misinterpreting the situation. For that reason advanced forward looking integrated safety systems have a high potential to improve safety for this group of road users. These systems combine reduction of impact speed by driver warning and/or autonomous braking with protective devices upon impact. Previous EU research projects resulted in systems which are gradually entering the market. However, such new systems have to be widely deployed in the marketplace to realise their potential benefits. The objective of the European AsPeCSS project in the 7 th research framework ( is to contribute towards improving the protection of vulnerable road users, in particular pedestrians and cyclists by developing harmonised test and assessment procedures for forward looking integrated pedestrian safety systems. The outcome of the project will be a suite of tests and assessment methods as possible input to future regulatory procedures and consumer rating protocols, such as Euro NCAP. Implementation of such procedures and protocols will promote widespread introduction of such systems in the vehicle fleet, which may result

2 2 Modelling of the Driver s Braking Behaviour for Assessment Methodology of Active Pedestrian Protection Systems in a significant reduction of fatalities (30% pedestrians; 20% cyclists) and seriously injured (50% pedestrians; 20% cyclists) among these vulnerable road users. The test procedure to be developed shall combine both active and passive safety assessment by measuring the impact velocity reduction in a dedicated set of test scenarios and calculating the injury severity taking passive safety into account. During active system test driver reaction is simulated by means of a brake robot. Since the driver s braking behaviour has a significant influence on the impact velocity a transparent and well validated modelling approach is crucial for the acceptance of the test procedure. 2.3 Model Development The model development described in the following focuses on the methodology of creating an application-specific driver model rather than describing the resulting model. Starting from the requirements the different development steps are discussed. Additionally some examples are given in order to illustrate the methodology Requirements and Approach Figure 2-1 shows the integration of the driver model into the system assessment methodology. In a first step the system response without driver reaction has to be determined. This is to identify the type and properties of the warning, as well as its timing. Based on this information the warning strategy is evaluated by assessing the perceptibility by means of criteria which are derived from literature and validated in experiments. The gathered information is fed into the underlying driver model which defines the dependency between the input parameters and the resulting brake pedal force curve. Finally the system benefit is evaluated by comparing the impact conditions with and without using the active safety system with a brake robot applying the brake pedal force curve calculated by the model. Determine System Response without Driver Reaction Type of Warning Timing of Warning Perceptibility Assessment Perceptibility of Warning Model Definition and Parameterization Driver Properties Driver Model Brake pedal force Brake Pedal Force vs. Time Active Safety Assessment with/without System Criticality System Benefit Figure 2-1: Step by step approach for driver model development

3 2.3 Model Development The test procedure described above results in various requirements concerning the driver model. On the one hand it is necessary to keep the model as simple as possible in order to achieve a transparent rating and reduce the parameters to be validated. On the other hand different warning strategies resulting in different perceptibility levels as well as other relevant factors shall be covered. As described by Summala [1] for example the driver reaction depends on the situation criticality. Furthermore, according to Wogalter [2] a valid reproduction of the driver behaviour cannot be applied without taking the driver s individual characteristics, such as age or task familiarity, into account which therefore also have to be incorporated into the model. As model output, it is desired to receive a brake pedal force curve, which is applicable to a braking robot in order to simulate the driver behaviour in active safety assessment. Varying driver characteristics shall be covered by using a representative set of parameters. Based on these requirements the model development is carried out in the following steps. At first perceptibility criteria are derived from literature. This allows for systematic analyses of warning design. In a second step a modelling approach to calculate the brake pedal force curve based on the perceptibility criteria and other influencing factors is selected. Following, the effect of influencing factors on the model parameters is determined systematically and finally perceptibility criteria and the parameterised model will be validated in a driving simulator study. Dividing the model development process into these steps reduces the complexity of the respective subtasks and allows for a structured methodology Perceptibility Assessment In order to incorporate the perceptibility of different warning strategies and designs into the assessment methodology, a perceptibility evaluation is crucial. Based on literature review perceptibility criteria have been collected and evaluated. Since these criteria cover numerous aspects of perceptibility and contain a lot of detailed information, a simplification has to be performed in order to achieve a simple and transparent rating for the test procedure and to easily determine the influence of the perceptibility criteria on the model parameters. This is achieved by combining multiple aspects of perceptibility to a limited number of perceptibility criteria. A short overview of the findings is given in the following. In general, three different channels for information perception are used in vehicles. Information is presented in a visual, audible or haptic manner. For each of these channels there are several degrees of freedom for designing the human-machine interface. Visual stimuli are mainly characterized by their size, position, color and brightness [3]. Furthermore flashing signals lead to a reduction of driver s reaction time [4]. The perception of audible alarms is influenced by their frequency (or multiple frequencies) and amplitude. But also the position of the sound source has an effect on the driver reaction [3], [5]. Additionally different types of haptic warnings can be applied. The stimulus can be induced by any part of the car, which is in contact to the driver. This can be achieved by e. g. using a haptic pedal [6], steering wheel vibrations [7], seat vibrations [8] as well as a brake pulse to apply a slight deceleration, which has the additional advantage of slowing the car down and thus extending the time for driver reaction [9].

4 2 Modelling of the Driver s Braking Behaviour for Assessment Methodology of Active Pedestrian Protection Systems Although there are several studies giving quantitative influence of different perceptibility criteria, for the approach described in this paper multiple criteria are combined. This leads to a reduction of the number of influencing factors and thus to a simplification of the resulting model. For example all requirements to a visual warning are combined to a single perceptibility criterion for visual warnings. If this criterion is fulfilled, the corresponding parameter set is used for the driver model. The collected data on warning perceptibility is the basis for determining influencing factors and their effect on the model parameters, which is described in detail in chapter Model Definition The driver model to be developed has to allow for reproducing the driver s brake pedal force as exact as possible while using a limited number of parameters. In order to avoid the necessity to costly validate a newly developed model, an existing approach from literature shall be adopted. After a brief summary of the state of the art in driver modelling this model selection is described below. Since the middle of the 20 th century numerous research activities have been dealing with driver modelling and human modelling in general respectively. The first approaches were aiming at describing the human behaviour, especially with regard to pilot applications, in control tasks and thus were using system theory in order to determine the human performance in a control loop (e.g. Tustin [10], McRuer [11]). In recent years a lot of research has been carried out, aiming at replacing the black box models from control theory by detailed cognitive models of the driver s information processing. Hamilton and Clarke [12] for example model the driver by a so called recognize-act-cycle, which consists of the four steps perceive, recognize, decide and act in combination with the driver s memory and knowledge. Thus, it can be distinguished between functional models basing on psychology, which describe the cognitive processes causing the driver reaction on the one hand and descriptive models, which are mainly affected by engineering and only describe the driver reaction itself, on the other hand. Taking the requirements mentioned in chapter into account, the driver model for active safety assessment has to be simple (using a low number of parameters) and well validated. Instead of a holistic modelling of the driving task only the brake pedal force has to be modelled quantitatively. For these reasons the crossover model developed by McRuer [11] has been chosen. This model differs from former approaches by neglecting the vehicle transfer function and incorporating it into a common model of the driver-vehicle behaviour. This is due to the fact that the driver creates an internal model of the vehicle transfer function and adjusts his control performance accordingly. With respect to the application in the AsPeCSS project another important argument for using the crossover model is the applicability for the psychomotor domain, which has been proven by Jürgensohn [13]. It has been shown, that the model is suitable to describe human movement, which can be considered as a secondary control task. The crossover model has been validated in other studies, such as those by Elkind [14] and Jackson [15].

5 2.3 Model Development Although there were other approaches to model the driver s control behaviour (e.g. optimal control models by Baron [16] and Sheridan [17]), the crossover model is even used in newer research, such as in [13]. The open-loop transfer function L(s) of the crossover model describing both the driver and the controlled element is given in Equation 2-1, using the gain ω c and time delay τ. ω c s L( s) = e τ s Equation 2-1 Figure 2-2 shows a comparison of the model output and the measured brake pressure, which has been recorded on a test track. Since brake pressure is proportional to braking force and a brake pressure sensor was already available in the vehicle for this test, pressure was used as control variable instead of force Brake Pressure [bar] p des = 110 bar Gain = 5.5 Integrator Delay ω c 1/s τ 20 Full braking with initial velocity of 60 kph Gain = male subject; 15 braking manoevres No investigation on reaction time t [s] Figure 2-2: Comparison of measured brake pressure and crossover model output The subject s task was to stop the vehicle as quickly as possible after receiving an appropriate command. There was no investigation on reaction time and therefore the model parameter time delay was not taken into account. As indicated in Figure 2-2 gain was determined as 5.5 and desired brake pressure as 110 bar. The figure shows a high congruence between model outputs and measured values, especially at the beginning of brake pressure build-up. However, there is a variation in maximum brake pressure with a minimum value of 96 bar, a maximum value of 128 bar and a standard deviation of 9.78 bar. The determined parameters are only valid for one

6 2 Modelling of the Driver s Braking Behaviour for Assessment Methodology of Active Pedestrian Protection Systems driver and are only to give a first impression of the applicability of the crossover model for modelling driver s braking behaviour Model Parameterisation and Validation In the preceding chapters both input parameters and model parameters have been discussed, but have not been connected. Therefore this chapter outlines a determination of relevant influencing factors and their effect on the model parameters. Figure 2-3: Approach for model parametrisation The basic approach for model parametrisation is shown in Figure 2-3. Starting from the perceptibility criteria and other input values, as driver properties or criticality, and taking into account the model parameters gain, time delay and maximum braking force, a matrix can be created, which links the influencing factors to the model parameters. Every cell of this matrix represents a potential dependency between an influencing factor and a model parameter. In a first step these cells are filled and consolidated according to information from literature. For example the influence of different types of warnings on driver s reaction time according to [18] can be included. This matrix serves as starting point for the detailed parameterisation which is performed in a driving simulator study. By means of systematic variation of influencing parameters their effect on the crossover model parameters will be determined. This results in final parameter sets, which cover all relevant influencing factors. 2.4 Conclusion and Perspective The AsPeCSS project aims at developing a harmonized test procedure for assessment of active pedestrian protection systems. Since these systems both provide warnings to the driver and intervene automatically, an assessment methodology cannot be applied without taking the driver s braking behaviour into account. In this paper the approach used for driver model development within the AsPeCSS project has been presented. Additionally some first results were given. By dividing the model development in some simpler subtasks, such as determination of perceptibility criteria or model definition, the complexity of the approach has been reduced. After deriving perceptibility criteria for visual, audible and haptic warnings from literature, the applicability of the crossover model for modelling the driver s braking behaviour has

7 2.5 References been demonstrated. Additionally an approach for connecting the influencing factors to the model parameters has been outlined. Further research has to be carried out to work out and conduct the driving simulator study, including scenario definition and design as well as performing and analyzing subject tests. An approach to determine the dependency between influencing factors and model parameters has been developed but not yet carried out and will be completed incorporating the results of the simulator study. 2.5 References [1] Summala, H. (2000) Brake Reaction Times and Driver Behavior Analysis, Transportation Human Factors 2, pp [2] Wogalter, M. S.; Conzola, V. C.; Smith-Jackson, T. L. (2002) Research-Based Guidelines for Warning Design and Evaluation, Applied Ergonomics 33, pp [3] Boff, K. R.; Lincoln, J. E. (1988) Engineering Data Compendium, Human Perception and Performance, Harry G Armstrong Aerospace Medical Research Laboratory, Ohio [4] Chan, A. H. S.; Ng, A. W. Y. (2009) Perceptions of Implied Hazard for Visual and Auditory Alerting Signals, Safety Science 47, pp [5] Baldwin, C. L.; May, J. F. (2011) Loudness Interacts with Semantics in Auditory Warnings to Impact Rear-End Collisions, Transportation Research Part F 14, pp [6] de Rosario, H.et al. (2010) Efficacy and Feeling of a Vibrotactile Frontal Collision Warning Implemented in a Haptic Pedal, Transportation Research Part F 13, pp [7] Suzuki, K.; Jansson, H. (2003) An Analysis of Driver s Steering Behaviour during Auditory or Haptic Warnings for the Designing of Lane Departure Warning System, JSAR Review 24, pp [8] Fitch, G. M. et al. (2011) Driver Comprehension of Multiple Haptic Seat Alerts Intended for Use in an Integrated Collision Avoidance System, Transportation Research Part F 14, pp [9] Brown, S. B. et al. (2005) Effects of Haptic Brake Pulse Warnings on Driver Behavior during an Intersection Approach, Proceedings of the Human Factors and Ergonomics Society 49 th Annual Meeting [10] Tustin, A. (1947) The Nature of the Operator s Response in Manual Control, and its Implications for Controller Design, Convention on Automatic Regulators and Servo Mechanisms

8 2 Modelling of the Driver s Braking Behaviour for Assessment Methodology of Active Pedestrian Protection Systems [11] McRuer, D. T. et al. (1965) Human Pilot Dynamics in Compensatory Systems, Franklin Institute, Technical Report, Ohio [12] Hamilton, W. I.; Clarke, T. (2005) Driver Performance Modeling and its Practical Application to Railway Safety, Applied Ergonomics 36, pp [13] Jürgensohn, T. (1997) Hybride Fahrermodelle, PhD Thesis, Berlin University of Technology [14] Elkind, J. I.; Miller, D. C. (1967) Adaptive Characteristics of the Human Controller of Time-Varying Systems, AF Flight Dynamics Laboratory, Technical Report [15] Jackson, G. A. (1969) A Method for the Direct Measurement of Crossover Model Parameters, IEEE Transactions of Man-Machine-Systems 10, pp [16] Baron, S.; Kleinman, D. L. (1969) The Human as an Optimal Controller and Information Processor, IEEE Transactions of Man-Machine-Systems 10, pp [17] Sheridan, T. B. (2004) Driver Distraction from a Control Theory Perspective, Human Factors 46, pp [18] Forkenbrock, G. et al. (2011) A Test Track Protocol for Assessing Forward Collision Warning Driver-Vehicle Interface Effectiveness, NHTSA, Technical Report, East Liberty