A cooperative control approach for autonomous vehicles is developed in order to perform different complex traffic maneuvers, e.g., double lane-switching or intersection situations. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles and then solved using a nonlinear Model Predictive Control (MPC) technique, where the distributed approach is used to make the problem computationally feasible in real-time. To provide safety, a collision avoidance constraint is introduced, also in a distributed way. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. In addition, a compatibility constraint is defined that takes collision avoidance into account but also ensures that each vehicle does not deviate significantly from what is expected by neighboring vehicles. The method allows us to construct a cost function for several different traffic scenarios. The asymptotic convergence of the system to the desired destination is proven, in the absence of uncertainty and disturbances, for a sufficiently small MPC control horizon. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.

A dynamic obstacle avoidance Model Predictive Control (MPC) method is introduced for autonomous driving that uses deep learning technique for velocity-dependent collision avoidance in unknown environments. The objective of the method is to control an autonomous vehicle in order to perform different traffic maneuvers in a safe way with maximum comfort of passengers, and in minimum possible time, accounting for maneuvering capabilities, vehicle dynamics, and in the presence of traffic rules, road boundaries and static and dynamic unknown obstacles. Here, by defining local coordinates and collision regions, the dynamic collision avoidance problem is translated into a static collision avoidance problem which makes the method easier and faster to be solved in dynamical environments. In order to provide safety, an ensemble of deep neural networks is used to estimate the probability of collision and to form an uncertainty-dependent collision cost which prioritizes between mission and safety. The collision cost is a product of the probability of collision and vehicle’s velocity in the directions with high collision-risk. The dynamic obstacle avoidance optimization method minimizes the velocity in the obstacle cones where the probability of collision is high or in unfamiliar environments, and increases the velocity when probability and variation in predicted values of the ensemble are low. The predicted trajectory from MPC is used in learning procedure in order to assign labels that makes it possible to predict the collision in advance. Simulation results show that the proposed method has good adaptability to unknown environments.

This paper describes a decoupled sampling-based motion-planning method, based on the rapidly-exploring random tree (RRT) approach, that is applicable to autonomous vehicles, in order to perform different traffic maneuvers. This is a two-step motion-planning method including path-planning and motion timing steps, where both steps are sampling-based. In the path-planning part, an improved RRT method is defined that increases the smoothness of the path and decreases the computational time of the RRT method; it is called smooth RRT, SRRT. While some other RRT-based methods such as RRT can perform better in winding roads, in the problem of interest in this paper (which is performing some regular traffic maneuvers in usual urban roads and highways where the passage is not too winding), SRRT is more efficient since the computational time is less than for the other considered methods. In the motion timing or velocity-tuning step (VTS), a sampling-based method is introduced that guarantees collision avoidance between different vehicles. The proposed motion-timing algorithm can be very useful for collision avoidance and can be used with any other path-planning method. Simulation results show that because of the probabilistic property of the SRRT and VTS algorithms, together with the decoupling feature of the method, the algorithm works well for different traffic maneuvers.

In this paper, a cooperative fuel and comfort efficient control for autonomous vehicles is presented in order to perform different traffic maneuvers. The problem is formulated as an optimal control problem in which the cost function takes into account the fuel consumption and passengers comfort, subject to safety and speed constraints. The optimal solution takes into account the comfort and fuel consumption, which is obtained by minimizing a jerk, an acceleration, and a fuel criterion. It is shown that the method can be applied to control different groups of vehicles in different traffic scenarios. Simulation results are used to illustrate the generality property and performance of the proposed approach.

This paper describes a cooperative control method for autonomous vehicles, in order to perform different traffic maneuvers. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles with an identified model and then solved using nonlinear Model Predictive Control (MPC). The distributed approach has been used in order to make the problem computationally feasible to be solved in real-time. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. The constraints on the control inputs ensure the comfort of passengers. The method allows us to construct a cost function for several different scenarios in which safety and performing the maneuver constitute two terms of the integrated cost of the finite horizon optimization problem. To provide safety, a potential function is introduced for collision avoidance. Simulation results show that the distributed algorithm scales well with increasing number of vehicles. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.