In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm.

This paper presents an analysis of the relationship of particle velocity and convergence of the particle swarm optimization. Its premature convergence is due to the decrease of particle velocity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An improved algorithm which introduces a velocity differential evolution (DE) strategy for the hierarchical particle swarm optimization (H-PSO) is proposed to improve its performance. The DE is employed to regulate the particle velocity rather than the traditional particle position in case that the optimal result has not improved after several iterations. The benchmark functions will be illustrated to demonstrate the effectiveness of the proposed method.

The t(14;18)(q32;q21) involving the immunoglobulin heavy chain locus (IGH) and the MALT1 gene is a recurrent abnormality in mucosa-associated lymphoid tissue (MALT) lymphomas. However, the nucleotide sequence of only one t(14;18)–positive MALT lymphoma has been reported so far. We here report the molecular characterization of the IGH-MALT1 fusion products in 5 new cases of t(14;18)–positive MALT lymphomas. Similar to the IGH-associated translocations in follicular and mantle cell lymphomas, the IGH-MALT1 junctions in MALT lymphoma showed all features of a recombination signal sequence–guided V(D)J-mediated translocation at the IGH locus. Furthermore, analogous to follicular and mantle cell lymphoma, templated nucleotides (T-nucleotides) were identified at the t(14;18)/IGH-MALT1 breakpoint junctions. On chromosome 18, we identified a novel major breakpoint region in MALT1 upstream of its coding region. Moreover, the presence of duplications of MALT1 nucleotides in one case suggests an underlying staggered DNA-break process not consistent with V(D)J-mediated recombination. The molecular characteristics of the t(14;18)/IGH-MALT1 resemble those found in the t(14;18)/IGH-BCL2 in follicular lymphoma and t(11;14)/CCND1-IGH in mantle cell lymphoma, suggesting that these translocations could be generated by common pathomechanisms involving illegitimate V(D)J-mediated recombination on IGH as well as new synthesis of T-nucleotides and nonhomologous end joining (NHEJ) or alternative NHEJ repair pathways on the IGH-translocation partner.

In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

In this paper, the finite-time consensus protocols are designed to solve the problems of unmeasured velocity and disturbances in heterogeneous multi-agent systems, in which the communications among agents are described by a directed graph. The proposed consensus protocols use only position measurements. The velocity of the agents is estimated according to the position measurements of multi-agent systems. The integral sliding mode control is adopted to eliminate the bounded disturbances of the systems and shorten the time to achieve consensus. The sufficient conditions for finite-time consensus are obtained by employing Lyapunov stability theory. Finally, simulations are provided to verify the effectiveness of the proposed schemes.