Abstract
Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement.
Highlights
Owing to increasing demand for driving stability and safety, Advanced Driver Assistant Systems (ADAS) have developed rapidly in our daily life, which have significantly improved the safety, performance and efficiency of vehicles in various complex driving environments [1]
We propose an adaptive parameter Interacting Multiple Model unscented Kalman filter algorithm (IMM-AUKF) to achieve accurate real-time target vehicle state estimation performance
When the vehicle-mounted sensor tracks the state of the preceding vehicle, due to the interference of various factors, the data filtered by the UKF may contain other noise values, which will inevitably increase the error of the filtering and noise reduction
Summary
Owing to increasing demand for driving stability and safety, Advanced Driver Assistant Systems (ADAS) have developed rapidly in our daily life, which have significantly improved the safety, performance and efficiency of vehicles in various complex driving environments [1]. We propose an adaptive parameter Interacting Multiple Model unscented Kalman filter algorithm (IMM-AUKF) to achieve accurate real-time target vehicle state estimation performance. Φk T+hωiskm· Todel can efficiently describe the true motion of a veh icle in the road, since the truωektrajectory can be considered that is consisted of segments where the turn rate and the aωwcckhe-ecleroTermahTtipisoiosmnnterhoenedmtetiolamfcinaEencqionuentfaefistrticvaoianennlt.(bt3Hley)toiwdsweeceselcvonresiterbw,etnootohszteeeeqrttoruhu,iesewnetmhixaipollertsiectoshansinisoosi.nfsascuavenehcbaiecnldebeiegneetnaheserilarytoeradedsw,oslhivneecndetbhthyee atsrsuuemtrianjgectthoartythcaenvbeehiccolenskiedeepresddrthivaitnigs icnonassitsrtaeidghotf lsiengem, aenndtstwhehneEreqtuhaetitounrn(3r)aitsesainmdptlhi-e fiaecdcetloerEaqtuioantiorenm(4a)i.n constant Note this expression can be degenerated when the ωk-component of Equation (3) is close to zero, while this issue can be resolved by assuming that the vehicle keeps driving in a straight line, and Equation (3) is simplified to Equation (4). Pr1 . . . prr IMM-AUKF algorithm are used to achieve recursive estimation, each recursion is mainly divided into the following four steps
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
