基于 LGSVL/Apollo 的网络延迟攻击下自动驾驶车辆定位估算

An unbiased finite impulse response filter (UFIR) under delay model was proposed to improve the accuracy of autonomous vehicle location estimation algorithm under delayed attack. A vehicle kinematics model under delayed attack was established and extended to a finite length time window. A batch and iterative forms of UFIR algorithms were derived. The embedding position of the algorithm was selected by analyzing the data flow of Apollo functional modules. A co-simulation test platform was built based on LG Silicon Valley Lab (LGSVL) Simulator and Apollo system, and conducted experiments. The results show that compared with the Kalman filter (KF), the algorithm has higher estimation accuracy, faster response speed, smaller fluctuation amplitude, and stronger robustness when the delay data changes greatly. The estimation effect is great when the data delay time is less than or equal to 1 s. Therefore, the result verifies the feasibility of the autonomous driving simulation experiment based on LGSVL and Apollo system.