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.

The Oriented FAST and Rotated BRIEF (ORB) algorithms have been improved, and a new method for calculating descriptors based on symmetrical multicentric weighted binary encoding is proposed, which enhances the robustness of feature points. This method employs a string of binary descriptors to encode the features and uses the multiple descriptor centre strategy to sample descriptors at the feature point and on the symmetrical circumference around it. Furthermore, a weighted summation is introduced in the descriptor calculation process to address the noise in the image during the sampling process. Specifically, the pixel values around the sampled point and the sampled point itself are combined using a certain weight to produce the final pixel value of the sampled point. The reliability of the descriptor is enhanced by introducing the pixel information around the sample point while solving the noise problem. Our method makes full use of the pixel information in the various parts of the descriptor sampling region to improve the distinguishability of the descriptors. We compare it with the ORB algorithm and experimentally show that the feature extraction method achieves better matching results with almost constant computation time.