| Vehicle active safety system was used more frequently in different vehicles then passivevehicle safety system. During the process of traffic conflict, vehicle active system can warnthe driver or control vehicle at early stage, so severity traffic conflict or accidents can beavoid. For vehicle safety, when existing traffic conflict, vehicle active safety system shoulddistinguish it as early as possible. From a time sequence view, if the next running state ofvehicle can be predicted by uding present state, then the coming soon traffic conflict can bepredicted. Based on this, the operation time of vehicle active system can improved. Runningstate characterization parameters of vehicle and traffic environment were different acoordingto time or others, so how to predict vehicle running state and traffic environment was a keytechnology while design vehicle active safety systems.Aming at the parameters predict requirements of vehicle active safety system, differentkinds of sensors were used to establish a test vehicle for data capture, vehicle speed and otherparameters can be captured in-phase. Ten drivers were called to drving this test vehicle indifferent road conditions, and large amout of vehicle running state data were obtained.Considering the real requirement of vehicle active system, the main reaserch content of thispaper were list as following:1. Time to line crossing (TLC) predict model during lane change process was bringingforward base on geometry analysis. Distance between vehicle and lane line was used toanalyzing the geometry characteristic duing lane change. By using vehicle-road geometrymodel, yaw angle predict method of vehicle during lane change was established. Aiming atstraight road and curve road, and considering lane change direction and curve direction, TLCpredict model of straight road and curve road was obtained. Real road test data was used toanalyzing the predict accuracy. Test results shows that the predict error was limited around0.Among all test results, predict error of straight road equal or less than0.1seconds achived78.3%, the similar result of curve road was80.8%. Predict error of two models meets thenormal distribution.2. By establishing vehicle-road geometry model, speed and yawrate were used to estimatethe road curvature. Based on this, distinguish model among availability target, latency availability target, and inefficacy target of ACC system was obtained. Single target test,Multi-target test, and multi-target state exchange test were carried out to test the accuracy ofthis model. Test result shows that the model can distinguish three types target accurately.Based ont this, fuzzy weighted evaluated model of target state exchange were estblished byuding target vehicle speed, target vehicle head time, target vehicle lateral moveing and otherparameters. Test results shows that the predict accuracy of different target state exchangeexceed90%.3. Aiming at the predict requrment of own vehicle, two degrees of freedom linear vehiclemodel was used. Fuzzy Petri net theory was used to estblishe vehicle running trajectorypredict model. Vehicle lateral moving, portrait moving state, pitching angle speed, and listangle speed were treated as input variables to obtaining vehicle running state predict model.Own vehicle speed, yawrate and running trajectory and other parameters were predict. Amingat the predict shortage of BP NN model, Bayesian filters was used to optimizing the predictresults, and the predict accuracy was improved from83.6%to82.4%.The research was sponsored by National Natural Science Foundation (51178053and61374196), Chang Jiang Scholars and Innovative Team Development Plan Program of theMinistry of Education (IRT1286). |
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