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Autonomous Avoidance Maneuvers for Vehicles using Optimization

To allow future autonomous passenger vehicles to be used in the same driving situations and conditions as ordinary vehicles are used by human drivers today, the control systems must be able to perform automated emergency maneuvers. In such maneuvers, vehicle dynamics, tire-road interaction, and limits on what the vehicle is capable of performing are key factors to consider. After detecting a static or moving obstacle, an avoidance maneuver or a sequence of lane changes are common ways to mitigate the critical situation. For that purpose, motion planning is important and is a primary task for autonomous-vehicle control subsystems. Optimization-based methods and algorithms for such control subsystems are the main focus of this thesis. Vehicle-dynamics models and road obstacles are included as constraints to be fulfilled in an optimization problem when finding an optimal control input, while the available freedom in actuation is utilized by defining the optimization criterion. For the criterion design, a new proposal is to use a lane-deviation penalty, which is shown to result in well-behaved maneuvers and, in comparison to minimum-time and other lateral-penalty objective functions, decreases the time that the vehicle spends in the opposite lane. It is observed that the final phase of a double lane-change maneuver, also called the recovery phase, benefits from a dedicated treatment. This is done in several steps with different criteria depending on the phase of the maneuver. A theoretical redundancy analysis of wheel-torque distribution, which is derived independently of the optimization criterion, complements and motivates the suggested approach. With a view that a complete maneuver is a sequence of two or more sub-maneuvers, a decomposition approach resulting in maneuver segments is proposed. The maneuver segments are shown to be possible to determine with coordinated parallel computations with close to optimal results. Suitable initialization of segmented optimizations benefits the solution process, and different initialization approaches are investigated. One approach is built upon combining dynamically feasible motion candidates, where vehicle and tire forces are important to consider. Such candidates allow addressing more complicated situations and are computed under dynamic constraints in the presence of body and wheel slip. To allow a quick reaction of the vehicle control system to moving obstacles and other sudden changes in the conditions, a feedback controller capable of replanning in a receding-horizon fashion is developed. It employs a coupling between motion planning using a friction-limited particle model and a novel low-level controller following the acceleration-vector reference of the computed plan. The controller is shown to have real-time performance.

Pavel Anistratov


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