Automotive challenge

Technical description summary

The goal of this challenge is to drive a given vehicle down a short, curved road segment, in such a way that the vehicle “drifts” as impressively as possible. To achieve this, contestants will need to design a control algorithm which intentionally drives the vehicle into an unstable equilibrium state and maintains this state through careful application of control inputs.

The algorithm will control a vehicle model, parametrized to reflect a drift car powered by electric motors. This model will feature realistic tyre models with lateral-longitudinal force coupling, vertical force dependency and saturation. The vehicle body and suspension will be simplified, to limit the scope of the challenge. The parameters of the vehicle model will be made available to all contestants, along with references to the equations the model was based on.

The vehicle is controlled by applying torque to the wheels and by rotating the steering wheel. Torque can be applied to any of the four wheels, in the positive or negative direction. The vehicle’s powertrain has a limited amount of torque it can supply per wheel, and the steering wheel can only be rotated up to a fixed maximum angle. Both torque and steering angle are not changed instantaneously – there is a maximum rate of change for both values.

The drift maneuver is judged objectively. The model computes and continuously outputs a cost function, whose value depends on:

• Δy – the lateral offset of the vehicle Center of Gravity from the road centreline
• β – the sideslip angle of the vehicle
• v_x – the vehicle longitudinal speed

The challenge will consist of two stages. For Stage A, the contestants will be supplied with a vehicle model in the form of a Functional Mockup Unit (FMU) – a component based on the open FMI standard. This model can be integrated into a number of different simulation environments, including Matlab/Simulink. Contestants will be judged on the performance of their algorithm as applied to the provided vehicle model, according to the above described cost function. The top three teams will proceed to Stage B, and win a paid trip to Zadar to compete at the Conference.

Stage B will consist of implementing the algorithm in a format suitable for execution on a Rapid Development Platform (RDP), provided by an industry partner. The algorithm will be implemented in Simulink, and translated automatically to platform-compatible code using software tools provided by the industry partner. At the Conference proper, the finalists from Stage A will be provided with an FMU containing a differently parametrized vehicle model. In the time available, contestants will need to re-tune their algorithm and run it on the RDP, with the vehicle implemented on a Hardware In the Loop (HIL) test rig.

All resources related to the challenge are available in the following Github repository: https://github.com/rimac-technology/med2018-automotive-challenge