Design and Optimization of Nonlinear Observers for Road Curvature and State Estimation in Automated Vehicles

The estimation of the state of lateral dynamics of a vehicle is usually performed using information gathered by several sensors, such as cameras, radars, odometers, and accelerometers. However, some quantities and their variations cannot be measured directly in all driving situations whereas they are required for vehicle control. Among these unmeasured quantities, one can find road curvature. This paper proposes to apply nonlinear observers to provide such information, in addition to other quantities related to the vehicle state, in the context of Traffic Jump Pilot. The first contribution of this paper is to formulate the nonlinear dynamics model and then to design two nonlinear observers based on High-Gain and Sliding Mode techniques. Furthermore, in order to ensure their efficient applications in a real situation, an experimental methodology is developed in order to optimize their performances by finding an optimal set of gain values. In that respect, a large database of real driving conditions and different road curvature scenarios is recorded for the performances evaluation and then a recent multi-objective optimization algorithm is used to find the optimal tuning parameters of the observers. The effectiveness of the proposed methodology is illustrated through an application to a driving test, which is not in the database. The use of this optimization tool is a novelty in the context of nonlinear observation and control, which allows getting optimized observers that can be efficiently applied to experimental data.