Real-time trajectory planning for automated vehicle safety and performance in dynamic environments (original) (raw)
Related papers
2019
Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performancebased trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions:
2021
This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical s...
Model Predictive Trajectory Planning for Automated Driving
IEEE transactions on intelligent vehicles, 2019
In order to enable automated driving systems on the road several key challenges need to be solved. One of these issues is real-time maneuver decision and trajectory planning. This work introduces a general framework for maneuver and trajectory planning with model predictive methods. It discusses several representations of this framework in distinct complexity levels. In general, sophisticated models require to non-quadratic objective functions with nonlinear constraints, leading to increased computational complexities during calculation. Yet, oversimplified models can neither cope with vehicle dynamics in critical maneuvers nor do they represent complex traffic scenes with several maneuver options appropriately. A scheme to partition the trajectory space into homotopy regions is proposed. In each homotopy class linearization about a trajectory from this class is applied. Demonstrations by simulation and with experimental vehicles show the capability of the proposed method in selecting optimal maneuvers and trajectories. This is even valid during extreme maneuvers like in last moment collision avoidance.
Real - Time Trajectory and Velocity Planning for Autonomous Vehicles
Regular issue, 2021
Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lan...
Real-time optimal motion planning for automated road vehicles
IFAC-PapersOnLine
This paper presents a real-time optimal motion planner algorithm for road vehicles. The method is based on a cubic spline trajectory planner which is able to plan a set of vehicle motions driving from a given initial state to a required final state. Maximal dynamical feasibility and passenger comfort are ensured by minimizing the lateral acceleration and tracking errors as the vehicle moves along the trajectory. Tracking of the planned motion is realized during planning and execution as well by separate longitudinal and lateral controllers. Efficient implementation and small number of optimization variables enables real-time usage. The trajectory planner is first tested in a quasi real-time simulation environment and then under real working conditions at the dynamic platform of proving ground ZalaZone with a completely drive-by-wire Smart Fortwo. Measurement results are presented and analyzed in detail, and possible future research directions are mentioned.
Model Based Trajectory Planning for Highly Automated Road Vehicles
The aim of this paper is to present a local trajectory planning method based on nonlinear optimization that is able to generate a dynamically feasible, comfortable and customizable trajectory for highly automated road vehicles. The presented algorithm is able to consider the nonholonomic dynamics of wheeled vehicles and ensures the dynamical feasibility of the planned trajectory by the model-based prediction of the vehicle's motion. The behavior of the vehicle is simulated with closed loop trajectory tracking control which allows to generate not only the trajectory of the vehicle but also the reference signal inputs for the controllers. The direct planning of the reference signals enables the vehicle to run exactly on the generated trajectory and eliminates the delays related to the inertia of the system.
Model-predictive active steering and obstacle avoidance for autonomous ground vehicles
Control Engineering Practice, 2009
This paper presents a model-predictive approach for trajectory generation of unmanned ground vehicles (UGVs) combined with a tire model. An optimal tracking problem while avoiding collision with obstacles is formulated in terms of cost minimization under constraints. Information on obstacles is incorporated online in the nonlinear model-predictive framework as they are sensed within a limited sensing range. The overall problem is solved online with nonlinear programming. For the local path regeneration upon detecting new obstacles, the cost function is augmented using the obstacle information in two methods. The first method uses the distance from the UGV to the nearest detected obstacle, and the second method uses the parallax information from the vehicle about the detected obstacles. Simulation results in cluttered and dynamic environments show that the modified parallax method effectively reflects the threat of the obstacles to the UGV considering the dimension and state variables of the vehicle, showing clear improvements over the distance-based methods.
Local trajectory planning for autonomous vehicle with static and dynamic obstacles avoidance
2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021
Trajectory planning is one of the most complex tasks that should be accomplished in order to ensure vehicle autonomous driving. Trajectory planning can be classified into local and global planning. The purpose of local trajectory planning is to find the optimal trajectory to follow a global reference trajectory while avoiding obstacles in a smooth and comfortable way, within the constraints of road driving. This paper presents a trajectory planning algorithm that calculates a path according to a set of predefined way-points describing a global map. The predefined way-points provide the basic reference frame of a curvilinear coordinate system to generate candidate paths, which start with a transient phase, followed by a curve parallel to the road. Each candidate path, associated to a desired velocity profile, is evaluated via a cost function against several criteria including passenger's comfort, static and dynamic obstacles avoidance and overall trajectory tracking. The chosen trajectory is then applied to a full vehicle model using a coupled longitudinal/lateral controller validated on SCANeR studio (OKtal) simulator. A challenging test scenario of SCANeR studio is used to validate the proposed algorithm under Matlab.
Real-Time Motion Planning for Agile Autonomous Vehicles
Journal of Guidance, Control, and Dynamics, 2002
Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of xed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of nite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.
An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles
2013 European Control Conference (ECC)
We address the problem of real-time obstacle avoidance on low-friction road surfaces using spatial Nonlinear Model Predictive Control (NMPC). We use a nonlinear fourwheel vehicle dynamics model that includes load transfer. To overcome the computational difficulties we propose to use the ACADO Code Generation tool which generates NMPC algorithms based on the real-time iteration scheme for dynamic optimization. The exported plain C code is tailored to the model dynamics, resulting in faster run-times in effort for real-time feasibility. The advantages of the proposed method are shown through simulation.