Extended H∞ Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation

Estimation of vehicle motion is a pivotal requirement for autonomous vehicles. This paper proposes a robust ego-vehicle motion estimation to achieve precise localization and tracking, especially in the case of highly dynamic driving. An extended H ∞ filter, based on a kinematic motion model assuming constant turn-rate and acceleration is used to fuse LiDAR, IMU, and vehicle dynamic sensors’ measurements. Measurements from a real high-performance autonomous race car, the so-called DevBot 2.0, have been used to validate the fusion approach in a Roborace competition and compared to a standard Kalman-filter approach. The proposed estimation concept adapts the H ∞ robustness bound based on the innovation sequence of the filter. This provides very fast tracking when it comes to highly dynamic movement, but still achieves minimal estimation uncertainty in case of stationary conditions with lower innovation. Furthermore, a pure kinematic model is used, which is robust against vehicle parameters, changes in the tire-road conditions, and changes in driving maneuvers. The resulting estimation concept shows outstanding performance for considered autonomous race scenario and can be used for a wide range of different applications, such as highway driving, urban driving, platooning, etc.