Automated vehicles navigate through their environment by first planning and subsequently following a safe trajectory. To prove safer than human beings, they must ultimately perform these tasks as well or better than human drivers across a broad range of conditions and in critical situations. We show that a feedforward-feedback control structure incorporating a simple physics-based model can be used to track a path up to the friction limits of the vehicle with performance comparable with a champion amateur race car driver. The key is having the appropriate model. Although physics-based models are useful in their transparency and intuition, they require explicit characterization around a single operating point and fail to make use of the wealth of vehicle data generated by autonomous vehicles. To circumvent these limitations, we propose a neural network structure using a sequence of past states and inputs motivated by the physical model. The neural network achieved better performance than the physical model when implemented in the same feedforward-feedback control architecture on an experimental vehicle. More notably, when trained on a combination of data from dry roads and snow, the model was able to make appropriate predictions for the road surface on which the vehicle was traveling without the need for explicit road friction estimation. These findings suggest that the network structure merits further investigation as the basis for model-based control of automated vehicles over their full operating range.

Phase portraits provide control system designers strong graphical insight into nonlinear system dynamics. These plots readily display vehicle stability properties and map equilibrium point locations and movement to changing parameters and system inputs. This paper extends the usage of phase portraits in vehicle dynamics to control synthesis by illustrating the relationship between the boundaries of stable vehicle operation and the state derivative isoclines in the yaw rate-sideslip phase plane. Closed-loop phase portraits demonstrate the potential for augmenting a vehicle's open-loop dynamics through steering and braking. The paper concludes by applying phase portrait analysis to an envelope control algorithm for yaw stability and a sliding surface controller for stabilising a saddle point equilibrium in drifting.

Race car drivers can offer insights into vehicle control during extreme manoeuvres; however, little data from race teams is publicly available for analysis. The Revs Program at Stanford has built a collection of vehicle dynamics data acquired from vintage race cars during live racing events with the intent of making this database publicly available for future analysis. This paper discusses the data acquisition, post-processing, and storage methods used to generate the database. An analysis of available data quantifies the repeatability of professional race car driver performance by examining the statistical dispersion of their driven paths. Certain map features, such as sections with high path curvature, consistently corresponded to local minima in path dispersion, quantifying the qualitative concept that drivers anchor their racing lines at specific locations around the track. A case study explores how two professional drivers employ distinct driving styles to achieve similar lap times, supporting the idea that driving at the limits allows a family of solutions in terms of paths and speed that can be adapted based on specific spatial, temporal, or other constraints and objectives.

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