Automatic lane-changing is a complex and common task for autonomous vehicle control. In this study, a hierarchical decoupled path and velocity planning model for lane changing is proposed to enhance driving safety, comfort, and traffic efficiency. First, a parametric trajectory model is established based on the vehicle kinematic model, and the initial trajectory is solved quickly by the Sequential Quadratic Programming algorithm; in addition, the path optimization function is designed to optimize the trajectory curvature, and the distance-based velocity optimization method is used to improve the trajectory transverse, longitudinal acceleration, and jerk. To ensure the accuracy of path tracking, a comprehensive online trajectory optimization function is proposed according to tracking error fitting and vehicle reachability domain. The validation results demonstrate that the optimized path transverse velocity, acceleration, and jerk change curve are smoother, which meets the safety and comfort requirements of trajectory planning. Finally, the feasibility of the proposed trajectory planning is verified in a prototype vehicle real-world test.

The active sound design (ASD) technology is one of the most flexible methods to achieve the diverse design of automobile sound, in which the active sound synthesis algorithm is essential. However, in previous works, the discontinuity phenomena always appear in the synthesized sound. Thus, a new method of active engine sound synthesis based on cylinder pressure signals is proposed in this paper for the sound synthesis algorithm of ASD technique. Firstly, the index matrix of sound grains is constructed based on the periodicity of engine cylinder pressure signal. Then the frame length and frame shift are defined by tracing the time step of Revolutions Per Minute (RPM) of engine, and the hamming window function is constructed to extract sound grains. Subsequently, the overlap-and-add (OLA) method is used to realize the synthesis of engine sounds with different durations. In addition, the code program of proposed algorithm is written and the ASD prototype system is built respectively to synthesize the engine sounds. Finally, a similarity evaluation metric is defined to verify the effectiveness of proposed algorithm. The results show that our algorithm not only restitutes the main features of original sound, but also satisfies both the real-time feedback on the driving status and the continuity of synthesized sound. It contributes to the development of real-time ASD engineering for automobiles.

Both the stochastic traffic information and state of charge (SOC) greatly impact the plug-in parallel hybrid electric vehicle performance. Uncertain cycles and driving styles affect the effectiveness of velocity prediction and further cause the instability of SOC estimate. These uncertain stochastic factors interfere with the solution of torque demand in different degrees in each control cycle. To address this issue, a stochastic model predictive control (SMPC) considering short-term forecast optimal SOC is proposed. Firstly, multiple linear regression of engine and battery is developed for energy management strategy (EMS), respectively. Then, the velocity prediction model is developed based Markov chain considering the driver styles, and reference SOC is optimized by dynamic programming with the forthcoming information. Finally, the SMPC-based EMS with the short-term optimal SOC is constituted. The verification results show Markov based on driver styles has better predictive performance than radial basis function neural networks and back propagation neural networks. The fuel economy of the proposed strategy improves by about 11.8% compared with normal model predictive control and is close to that of the globally optimal dynamic programming. The test results indicate that the SMPC with the short-term optimal SOC can promote EMS to improve the fuel economy.