The continuously growing vehicle density on European roads leads to a higher risk for trafficparticipants to be involved in accidents. In order to mitigate this risk both for vehicle occu-pants as well as unprotected traffic participants, the automotive industry seeks for solutions inthe intelligent combination of active and passive safety systems towards an integral approach.Safety applications like an active emergency brake that can reduce the consequences of anaccident or even avoid a crash completely and predictive passive safety systems that featureoptimized deployment characteristics of restraint systems (airbags, belt pretensioners) bothdepend on anticipatory sensor signals concerning the vehicle environment in the pre-collision-phase as a basis for their crash prediction algorithms. The development, test and validation ofpredictive safety systems require efficient simulation-based methods in order to be able toachieve a large test space coverage and to generate reproducible sensor signals for the respec-tive test scenarios.In this paper a highly configurable and flexible method for the simulation-based developmentand testing of predictive safety algorithms is presented. The method is based on a synchroni-zed data connection between MATLAB/Simulink/Stateflow and "Virtual Test Drive" (VTD).MATLAB/Simulink/Stateflow allows the intuitive model-based rapid prototyping of safetyfunction algorithms using predictive sensor information as input data. These algorithms caneasily be transformed into ANSI/ISO C-compliant code for diverse hardware targets e.g. bythe Real-Time Workshop and tested in an identical form in the vehicle after the optimizationand validation process in the simulation environment.VTD consists of the components driving simulation, traffic simulation, visualization andsensor models, which supply the algorithms running in MATLAB/Simulink/Stateflow withthe required sensor input data concerning the virtual vehicle environment.This combination offers the possibility to easily implement a large variety of relevant trafficsituations and environmental conditions in order to test, optimize and validate the predictivesafety systems under repeatable conditions. Simulation data can be accessed via interfaces foran on-/offline data evaluation and visualisation by independent analysis applications. Thecomplete simulation environment can be distributed over several computers connected via IP-network and executed in real-time or on the basis of a common simulation time.The simulation environment was exemplarily used to test and optimize an anticipatory algo-rithm characterizing an imminent collision by the prediction of representative collision para-meters. The testing was done on the basis of a huge pool of characteristic pre-crash-scenariosstatistically representing the GIDAS database (German In-Depth Accident Study).     «

The continuously growing vehicle density on European roads leads to a higher risk for trafficparticipants to be involved in accidents. In order to mitigate this risk both for vehicle occu-pants as well as unprotected traffic participants, the automotive industry seeks for solutions inthe intelligent combination of active and passive safety systems towards an integral approach.Safety applications like an active emergency brake that can reduce the consequences of anaccident or even avoid a crash c...     »