Shift-scheduling calibration is important to the automobile industry, but it is repetitive and time-consuming; it is thus desirable to have a robot driver to automate this process. In this paper, we propose automating the calibration of shift-scheduling by using bionic optimization, i.e., particle swarm optimization (PSO), to guide the searching process and to integrate the driving styles into the calibration by equipping the robot drivers with personalized driver models. The personalized driver model is established by imitating the human driving behavior and is employed as a robot driver to conduct the driving cycle test, i.e., FTP-72 or US06, for candidate shifting schedules. The shifting performance is evaluated online via the computed performance index and/or AVL-Driver, regarding both driveability and fuel economy. Guided by PSO, candidate schedules are generated, tried, and evaluated until an optimal or near-optimal solution is obtained through iterations. Numerical experiments are presented to verify the feasibility and effectiveness of the proposed scheme. The shifting performance is improved by about 2% in computed performance index when compared with the base map. It is also suggested that personalized calibration is preferred if economically feasible.

Shift-scheduling calibration of automatic transmission (AT) vehicles is vital for both driving experience and automobile industry. Shifting schedules are usually calibrated with the consideration of fuel economy and drivability while neglecting the individual driving preference. In this work, we propose to exemplify the individual shifting preference by integrating the manual transmission (MT) shifting habit into the AT shift-scheduling calibration, where the habit is reflected as the shifting points and is available in most automated manual transmission (AMT) vehicles. The automated calibration of AT shifting schedules is directed by using the particle swarm optimization (PSO), during the virtual automobile cycle test, i.e., FTP-72. Candidate shifting-schedules are generated in the overlapped zone of MT shifting points and the space around the base map, and are evaluated on both shifting quality and fuel economy. Through iterations, the generated candidate shift schedules are tested and assessed until the overall performance reach the optimum. Experimental results are presented to show the effectiveness of the proposed method, which retains the shifting preferences as well as enhances the performance index by about 5%, 4%, and 2% for the drivers with aggressive, moderate, and mild styles, respectively.

Driving behaviour analysis is important for both intelligent transportation and public security. The authors propose to characterise driving behaviours by using the phase‐space reconstruction (PSR) and the pre‐trained convolutional neural network (CNN). PSR is first applied to the raw vehicle test data (VTD) to obtain the reconstructed trajectories. Second, the corresponding feature vectors are acquired by using the pre‐trained CNN. Third, the t ‐distributed stochastic neighbour embedding ( t ‐SNE) algorithm is applied to the feature vectors to validate their characterising ability. Finally, an index is proposed based on the aforementioned feature vectors for quantitative evaluation, i.e. driving style recognition and abnormal driving detection. Simulations are conducted to verify the effectiveness of the proposed scheme.

Most monocular simultaneous localization and mapping (SLAM)systems achieve good performance under the static environmentassumption, while they are unable to deal with dynamic environments.