Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving

One major issue in learning-based model predictive control (MPC) for autonomous driving is the contradiction between the system model's prediction accuracy and computation efficiency. The more situations a system model covers, the more complex it is, along with highly nonlinear and nonconvex properties. These issues make the optimization too complicated to solve and render real-time control impractical.To address these issues, we propose a hierarchical learning residual model which leverages random forests and linear regression.The learned model consists of two levels. The low level uses linear regression to fit the residues, and the high level uses random forests to switch different linear models. Meanwhile, we adopt the linear dynamic bicycle model with error states as the nominal model.The switched linear regression model is added to the nominal model to form the system model. It reformulates the learning-based MPC as a quadratic program (QP) problem and optimization solvers can effectively solve it. Experimental path tracking results show that the driving vehicle's prediction accuracy and tracking accuracy are significantly improved compared with the nominal MPC.Compared with the state-of-the-art Gaussian process-based nonlinear model predictive control (GP-NMPC), our method gets better performance on tracking accuracy while maintaining a lower computation consumption.Comment: 8 pages, 8 figure

Effects of alpha fetoprotein on escape of Bel 7402 cells from attack of lymphocytes

BACKGROUND: Involvement of AFP against apoptosis of tumor cell has been implicated in its evasion of immune surveillance. However, the molecular events of immune escape mechanisms are still unknown. The major observations reported here relate to a possible mechanism by which heptoloma Bel 7402 cells escape immune surveillance in vitro. METHODS: Western blotting and a well-characterized cofocal scanning image were performed to analyze the expression of Fas/FasL and caspase-3 in co-cultured Bel 7402 and Jurkat cells. RESULTS: After co-culture with Jurkat cells, up-regulated Fas and reduced FasL expression could be observed. Treatment with AFP could remarkably inhibit the elevated Fas and, whereas, induce the FasL expression in co-cultured Bel 7402 cells. Cells co-culture could induce the expression of caspase-3 in both cells line. The elevated caspase-3 in Bel 7402 cells was abolished following the treatment of AFP. The expression of caspase-3 was elevated in co-cultured Jurkat cells treated with AFP. No detectable change on the expression of survivin was examined in both cells line. Monoclonal antibody against AFP treatment alone did not obviously influence the growth of cells, as well as the expression of Fas/FasL and caspase-3. However, the effect of AFP could be blocked by antibody. CONCLUSIONS: our results provide evidence that AFP could promote the escape of liver cancer cells from immune surveillance through blocking the caspase signal pathway of tumor cells and triggering the Fas/FasL interaction between tumor cells and lymphocytes