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.
Motion Planning for Autonomous Vehicles
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.
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.
Human drivers navigate the roadways by balancing values such as safety, legality, and mobility. An autonomous vehicle driving on the same roadways as humans likely needs to navigate based on similar values. For engineers of autonomous vehicle technology, the challenge is then to connect these human values to the algorithm design.
Human drivers navigate the roadways by balancing values such as safety, legality, and mobility. The public will likely judge an autonomous vehicle by similar values. The iterative methodology of value sensitive design formalizes the connection of human values to engineering specifications. We apply a modified value sensitive design methodology to the development of an autonomous vehicle speed control algorithm to safely navigate an occluded pedestrian crosswalk.
One approach to motion control of autonomous vehicles is to divide control between path planning and path tracking. This paper introduces an alternative control framework that integrates local path planning and path tracking using model predictive control (MPC). The controller plans trajectories, consisting of position and velocity states, that best follow a desired path while remaining within two safe envelopes. One envelope corresponds to conditions for stability and the other to obstacle avoidance.
As autonomous vehicles enter public roads, they should be capable of using all of the vehicle's performance capability, if necessary, to avoid collisions. This dissertation focuses on facilitating collision avoidance for autonomous vehicles by enabling safe vehicle operation up to the handling limits. The new control approaches first rely on a standard paradigm for autonomous vehicles that divides vehicle control into trajectory generation and trajectory tracking.
Autonomous vehicle technologies offer potential to eliminate the number of traffic accidents that occur every year, not only saving numerous lives but mitigating the costly economic and social impact of automobile related accidents. The premise behind this dissertation is that autonomous cars of the near future can only achieve this ambitious goal by obtaining the capability to successfully maneuver in friction-limited situations.