Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved algorithm. First, this method improves the heuristic function of the traditional algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified algorithm and the traditional algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.

In this paper, an improved bidirectional RRT vehicle path planning method for smart vehicle is proposed. In this method, the resultant force of the artificial potential field is used to determine the search direction to improve the search efficiency. Different kinds of constraints are considered in the method, including the vehicle constraints and the vehicle driving environment constraints. The collision detection based on separating axis theorem is used to detect the collision between the vehicle and the obstacles to improve the planning efficiency. The cubic B-spline curve is used to optimize the path to make the path’s curvature continuous. Both simulation and experiment are implemented to verify the proposed improved bidirectional RRT method. In the simulation analysis, this paper’s method can generate the smoothest path and takes the shortest time compared with the other two methods and it can be adaptive to the complicated environment. In the real vehicle experiment, we can see from the test results that this paper’s method can be applied in practice on the smart electric vehicle platform; compared with others’ algorithm, this paper’s algorithm can generate shortest and smoothest path.

There are a variety of models and algorithms that solves classification problems. Among these models, Maximum Gaussian Mixture Model (MGMM) is a model we proposed earlier that describes data using the maximum value of Gaussians. Expectation Maximization (EM) algorithm can be used to solve this model. In this paper, we propose a multiEM approach to solve MGMM and to train MGMM based classifiers. This approach combines multiple MGMMs solved by EM into a classifier. The classifiers trained with this approach on both artificial and real life datasets were tested to have good performance with 10-fold cross validation.

Electromagnetic acoustic transducer (EMAT) is used as a noncontact sensor for acoustic communication in a stainless-steel plate channel. The acoustic wave multipath propagations within the channel cause intersymbol interference (ISI). ISI highly degrades the signal-to-noise ratio (SNR). The poor SNR will result in a low bitrate and a high bit error rate (BER). In this study, a test platform is designed to estimate the performance of the EMAT communication system. Two periodic-permanent-magnet (PPM) EMATs are designed to be used as a transmitter and a receiver. EMAT generates shear-horizontal (SH) wave propagating through the stainless-steel plate channel. We present methods to minimize the ISI effect and recover the message embedded in the received signals hampered by interference, reverberation and multipath effects. The root raised cosine (RRC) filters are introduced as a pulse-shaping filter for transmission and a matched filter for receiving messages. Amplitude Shift Keying (ASK) method is used for modulating the transmitted binary message. The pulse shaping and matched filters reduced the ISI caused by the undesirable multipath effect significantly to the extent that a 5-kbps bitrate can be achieved without any error (zero BER).

Aiming at precisely tracking an intelligent vehicle on a desired trajectory, this paper proposes an intelligent vehicle trajectory planning and control strategy based on an improved terminal sliding mold. Firstly, the traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem to improve the algorithm search efficiency. Secondly, an improved terminal sliding mode controller is designed. The controller comprehensively considers the lateral error and heading error of the tracking control, and the stability of the control system is proven by the Lyapunov function. Finally, the performance of the designed controller is verified by the Matlab-Carsim HIL simulation platform. The test results of the Matlab-Carsim HIL simulation platform show that, compared with the general terminal sliding mode controller, the improved terminal sliding mode controller designed in this paper has higher control accuracy and better robustness.

Vehicles will face different working conditions during the use, and different working conditions have different requirements for vehicle functions, which results in many subdivided models. An off-road vehicle is a subdivision model produced to adapt to complex road conditions. In order to adapt to complex road conditions, vehicles should have a good passing ability, small size, and good flexibility. The six-wheeled vehicle has both good passing ability and small volume, which is the best choice for off-road vehicles. The design of the steering system becomes the key step to improve the flexibility of the six-wheeled vehicle. This paper mainly designs an independent steering system for a six-wheel electric vehicle with higher flexibility. The system is designed for six-wheel electric vehicles driven by six in-wheel motors. It mainly includes mechanical steering system and electronic control steering system. Both mechanical steering systems and electronic control steering system have multiple steering modes. Firstly, this paper introduces the various steering modes of the mechanical steering system and the electronic control steering system. Secondly, a CAD model is established by using the software Solidworks, and the system structure is introduced in detail combined with the CAD model. Finally, a kinematics model is established and calculated. The calculation results showed that the steering system can significantly improve the flexibility of the vehicle, so that the vehicle can complete the steering stably and quickly on complex road sections.