Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints

Distributed H∞ Controller Design and Robustness Analysis for Vehicle Platooning Under Random Packet Drop

This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded H∞ norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as γ gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the γ gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop

Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways

Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with safety constraints. It is shown that the decision model can replicate human drivers' discretionary lane-change decisions with up to 92% accuracy. Further proof of concept simulation of an overtaking manoeuvre is shown, whereby the actions of the simulated vehicle are logged while the dynamic environment evolves as per ground truth data recordings.Comment: 6 pages, 8 figures, The 2020 IEEE 92nd Vehicular Technology Conferenc

Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments using Robust MPC

Automated vehicles are increasingly getting mainstreamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, overtake, etc.) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway, motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: (i) it is free from nonconvex collision avoidance constraints, (ii) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion, and (iii) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for highspeed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment

Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC

Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment

Autonomous high-speed overtaking in structured environments

In this thesis, we design and develop controllers for trajectory planning and trajectory tracking to tackle autonomous high-speed overtaking for the next generation of autonomous vehicles. Both the controllers are developed for a JLR Range Rover Sport that is capable of autonomous driving functionalities. To assist with controller development, a high-fidelity vehicle model previously developed in IPG Carmaker is utilised that contains all the multi-body interactions and non-linear tyre characteristics. Trajectory Planning Autonomous high-speed driving is a safety-critical task and it is imperative that the planned trajectory of the vehicle can ensure safety (collision-avoidance) while computing smooth and feasible trajectories. We propose a trajectory planning framework that utilises information of the traffic vehicles to identify safe driving zones on the road using potential field functions and a robust model predictive controller for generating feasible trajectories that ensure the vehicle remains within the safe zones while performing the overtaking manoeuvre. The closed-loop performance of this controller is validated in a high-fidelity co-simulation environment. Trajectory Tracking The trajectory tracking controller is designed to ensure that the vehicle tracks the trajectory as closely as possible and preserves the lateral-yaw stability at all times. In this thesis, an Enhanced Model Reference Adaptive Control algorithm is used to design a generic lateral tracking controller for an autonomous vehicle. The control algorithm is applied to a vehicle path tracking problem and its tracking performance is investigated when subjected to external disturbances such as crosswind, road surface changes, modelling errors, and parameter miss-matches in a high-fidelity co-simulation environment. Combined Planning & Control Finally, the design of a combined motion planning & control scheme is carried out. The lateral tracking controller is augmented to include the dynamics of the steering actuator system and the updated tracking controller is combined with the RMPC based sophisticated path-planning framework to present a hierarchical closed-loop control architecture for autonomous overtaking. This architecture is implemented on the IPG CarMaker/Simulink environment and validated with different overtaking manoeuvring scenarios

Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance

This paper proposes an integrated trajectory planning based on Model Predictive Control (MPC) for designing collision-free evasive trajectory and a torque vectoring controller based on optimal control to ensure lateral-yaw stabilization in autonomous emergency collision avoidance under low friction and crosswinds on highways. The trajectory for performing the evasive manoeuvre is designed to minimise the deviation of the vehicle from the lane center while ensuring the vehicle remains within the road boundaries. The steering input computed from the MPC is used to steer the vehicle along the reference trajectory while the torque vectoring controller provides additional lateral-yaw stability. The integrated control framework was implemented on IPG Carmaker-MATLAB co-simulation platform and its efficacy was evaluated under different scenarios. Simulations performed for emergency collision avoidance at high speeds with low road friction and heavy crosswinds confirm the ability of the proposed closed-loop framework at successfully avoiding collisions with moving obstacles while ensuring that the controlled vehicle remains within its limits of stability. Furthermore, the robustness of the proposed control framework to variations in road friction changes is demonstrated by simulating an evasive manoeuvre at high-speeds for wide range of road friction conditions. Comparing the performance of the proposed control framework to a vehicle without the corrective actions available via torque vectoring highlight the additional benefits provided by the proposed closed-loop scheme at ensuring lateral-yaw stability under emergency scenarios