A system for limiting performance inconsistences of an electric vehicle during a race or other circumstance when consistent, high performance output is desired, such as by enabling a driver to selectively engage endurance and qualify drive modes to control a supply of electrical power used for driving the electric vehicle.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for performing vehicle control. Multiple target rotation rates for a vehicle shaft may be identified. A first actual rotation rate may be determined to exceed a first target rotation rate. In response, a computing system may send a first signal in order to cause a first component of a vehicle to limit the rate of rotation of the vehicle shaft. A second actual rotation rate may be determined to be below a second target rotation rate. In response, the computing system may send a second signal in order to cause the first component of the vehicle or a second component of the vehicle to increase the rate of rotation of the vehicle shaft.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for performing vehicle control. Multiple target rotation rates for a vehicle shaft may be identified. A first actual rotation rate may be determined to exceed a first target rotation rate. In response, a computing system may send a first signal in order to cause a first component of a vehicle to limit the rate of rotation of the vehicle shaft. A second actual rotation rate may be determined to be below a second target rotation rate. In response, the computing system may send a second signal in order to cause the first component of the vehicle or a second component of the vehicle to increase the rate of rotation of the vehicle shaft.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for performing vehicle control. Multiple target rotation rates for a vehicle shaft may be identified. A first actual rotation rate may be determined to exceed a first target rotation rate. In response, a computing system may send a first signal in order to cause a first component of a vehicle to limit the rate of rotation of the vehicle shaft. A second actual rotation rate may be determined to be below a second target rotation rate. In response, the computing system may send a second signal in order to cause the first component of the vehicle or a second component of the vehicle to increase the rate of rotation of the vehicle shaft.

Systems and methods are provided for determining vehicle configurations. The system can receive simulation data or actual driving data of a driving track corresponding to a driver and associate the simulation data or actual driving data with one or more driving parameters. A driver style can be determined based on the one or more driving parameters. A vehicle configuration can be determined for a vehicle of the driver based on the determined driver style. The system can display one or more vehicle settings associated with the vehicle configuration on a user interface.

A method of operating an autonomous racetrack driver coach and demonstrator in an autonomous vehicle employing operating systems for propulsion and maneuvering includes identifying a road course and mapping a velocity profile and a trajectory for the vehicle via a remote configurator. The trajectory defines a vehicle path around the road course and with the velocity profile minimizes the vehicle's lap time. The method also includes determining a presence of a human passenger/operator in the vehicle. The method additionally includes determining, via an electronic controller in communication with a remote detection source, localization of the vehicle on the road course. The method also includes determining vehicle velocity, acceleration, and heading relative to the mapped trajectory. Furthermore, the method includes operating the vehicle, with the human passenger/operator situated therein, to follow the mapped trajectory using feedback control of the operating systems in response to the determined localization, velocity, acceleration, and heading.

A system and method for optimizing the performance of an autonomous race car in real-time during a race event are disclosed. An autonomous race car controller unit is pre-fed with a first set of initial parameter values and a second set of initial parameter values. A set of sensors is configured for measuring a first and a second set of real-time parameter values after the starting of the race event. A performance optimization module is configured to generate a corrective course by receiving the first and second sets of real-time parameters and detecting the presence of errors between a control command given by the controller unit and its execution.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for performing vehicle control. Multiple target rotation rates for a vehicle shaft may be identified. A first actual rotation rate may be determined to exceed a first target rotation rate. In response, a computing system may send a first signal in order to cause a first component of a vehicle to limit the rate of rotation of the vehicle shaft. A second actual rotation rate may be determined to be below a second target rotation rate. In response, the computing system may send a second signal in order to cause the first component of the vehicle or a second component of the vehicle to increase the rate of rotation of the vehicle shaft.

Systems and methods for determining a maximum phase recovery envelope are disclosed herein. In one example, a system includes a processor and a memory having a vehicle control module. The vehicle control module includes instructions that, when executed by the processor, cause the processor to determine a critical point on a phase plane indicating a maximum defined recovery point a vehicle can recover from, perform forward and reverse simulations from the critical point to define outermost contours of a maximum phase recovery envelope using parameters and state of the vehicle, and cause the vehicle to operate within the maximum phase recovery envelope.

An AI-driven traction control system for race cars that dynamically adjusts ignition timing based on real-time RPM signal analysis. The system uses machine learning to continuously refine slip detection, eliminating the need for predefined slip thresholds. Unlike traditional methods relying on acceleration comparisons or cylinder deactivation, this system modifies ignition timing incrementally to maintain traction while maximizing power delivery. The AI model adapts based on real-world race conditions, making the system self-learning and fully autonomous. The invention applies to performance vehicles with MSD ignition systems or similar RPM-based signal inputs.