Professional drivers in drifting competitions demonstrate accurate control over a car's position and sideslip while operating in an unstable region of state-space. Could similar approaches help autonomous cars contend with excursions past the stable handling limits, thereby improving overall safety outcomes? As a first step towards answering that question, this paper presents a novel controller framework for automated drifting along a path. The controller is derived for the general case, without reference to a nearby equilibrium point.
Vehicle Dynamics and Control At The Limits of Handling
The problem of maneuvering a vehicle through a race course in minimum time requires computation of both longitudinal (brake and throttle) and lateral (steering wheel) control inputs. Unfortunately, solving the resulting nonlinear optimal control problem is typically computationally expensive and infeasible for real-time trajectory planning. This paper presents an iterative algorithm that divides the path generation task into two sequential subproblems that are significantly easier to solve.
A new method is presented for low-level steering control of autonomous vehicles. By tracking tire slip angle instead of steering angle, the new controller makes possible a more direct use of force-based high-level control schemes since uncertain, noisy measurements of the vehicle states do not have to be used to convert a desired tire slip angle to a commanded steer angle. Experimental data from a full-size vehicle show that this approach offers some advantages when combined with a force-based path tracking controller.
In emergency situations, autonomous vehicles will be forced to operate at their friction limits in order to avoid collisions. In these scenarios, coordinating the planning of the vehicle's path and speed gives the vehicle the best chance of avoiding an obstacle. Fast reaction time is also important in an emergency, but approaches to the trajectory planning problem based on nonlinear optimization are computationally expensive.
Contingency Model Predictive Control augments classical MPC with an additional horizon to anticipate and prepare for potential hazards.
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.
Autonomous vehicles have the potential to eliminate the vast number of motor-vehicle accidents that occur each year. However, as the burgeoning technology becomes more publicly available, self-driving cars will continue to encounter emergency situations. To maximize the vehicle's ability to navigate these situations safely, autonomous driving technology needs to be able to use all of the vehicle's performance capability.
Tire Modeling to Enable Model Predictive Control of Automated Vehicles From Standstill to the Limits of Handling
Model predictive control (MPC) frameworks have been effective in collision avoidance, stabilization, and path tracking for automated vehicles in real-time. These MPC formulations use a variety of vehicle models that capture specific aspects of vehicle handling, focusing either on low-speed scenarios or highly dynamic maneuvers. However, these models individually are unable to handle all operating regions with the same performance. This work introduces a novel linearization of a brush tire model that is affine, timevarying, and effective at any speed.