Dynamically integrated spatiotemporal-based trajectory planning and control for autonomous vehicles

In the literature, the intensive research effort has been made on the trajectory planning for autonomous vehicles, while the integration of the trajectory planner with the trajectory controller is less focused. This study proposes the spatiotemporal-based trajectory planner and controller by a two-level dynamically integrated structure. In the upper level, the best trajectory is selected among a group of candidate time-parameterised trajectories, while the target vehicle ending position and velocity can be satisfied. Then the planned trajectory is evaluated by checking the feasibility when the actual vehicle dynamic motion constraints are considered. After that, the lower level trajectory controller based on the vehicle dynamics model will control the vehicle to follow the desired trajectory. Numerical simulations are used to validate the effectiveness of the proposed approach, where the scenario of an intersection and the scenario of overtaking are applied to show that the proposed trajectory controller can successfully achieve the control targets. In addition, compared with the potential field method, the proposed method based on the four-wheel independent steering and four-wheel independent driving electric vehicle shows great advantages in guaranteeing the vehicle handling and stability

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

Path Planning for Autonomous Vehicle in Off-Road Scenario

The road topography information, such as bank angle and road slope, can significantly affect the trajectory tracking performance of the autonomous vehicle, so this information needs to be considered in the trajectory planning and tracking control for off-road autonomous vehicle. In this chapter, a two-level real-time dynamically integrated spatiotemporal-based trajectory planning and control method for off-road autonomous vehicle is proposed. In the upper-level trajectory planner, the most suitable time-parameterised trajectory with the minimum values of road slope and bank angle can be selected from a set of candidate trajectories. In the lower-level trajectory tracking controller, the sliding-mode control (SMC) technique is applied to control the vehicle and achieve the desired trajectory. Finally, simulation results are presented to verify the proposed integrated trajectory planning and control method and prove that the proposed integrated method has better overall tracking control and dynamics control performance than the conventional method both in the highway scenario and off-road scenario. Furthermore, the four-wheel-independent-steering (4WIS) and four-wheel-independent-driving (4WID) vehicle shows better tracking control performance than vehicle based on two-wheel model

er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars. This paper presents the complete software architecture used by team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modelling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed respectively second and third.Comment: Preprint: Accepted to Field Robotics "Opportunities and Challenges with Autonomous Racing" Special Issu

Light vehicle model for dynamic car simulator

Driving simulators have been becoming little by little a suitable tool oriented to improve the knowledge about the domain of driving research. The investigations that can be conducted with this type of tool concern the driver's behaviour, the design/control of vehicles, testing assistance systems for driving and the roadway infrastructure's impact. The benefits of simulation studies are many: lack of any real risk to users, reproducible situations, time savings and reduced testing costs. In addition, their flexibility allows to test situations that do not exist in reality or at least they rarely and randomly exist. The topic of the present work concerns the development of a brand new dynamic model for an existing car simulator owned by LEPSIS laboratory (Laboratoire d'Expliotation, Perception, Simulateurs et Silulations – Laboratory for Road Operations, Perception, Simulators and Simulations) belonging to COSYS (COmposants et SYStems), which is a department of IFSTTAR institute (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux – French Institute of Science and Technology for Transport, Spatial Planning, Development and Networks) site. Once uses and advantages of driving simulators are listed and described, imperfections and limitations of the existing driving vehicle model belonging to the two Degrees of Freedom (DoF) driving simulator of the laboratory are highlighted. Subsequently, structure of the brand new vehicle model, designed by means of Matlab Simulink software, are illustrated through the theoretical framework. Since the vehicle model must refer to a real one, an instrumented Peugeot 406 has been chosen because all its technical features are provided and inserted both on the present model and Prosper/Callas 4.9 by OKTAL software to create a highly sophisticated and accurate virtual version of the commercial car. The validation of this new vehicle model is performed, where the results returned by several different driving scenarios are compared with the ones provided by Prosper software. All the scenarios are simulated with both existing and new vehicle model uploaded in the driving simulator, and the outputs are subsequently compared with the ones returned by Prosper in order to demonstrate the improvements done. Finally, being the number of outputs provided by the new model definitively higher with respect to previous one, additional validations concerning the further results are accomplished

Towards a Decision-Making Algorithm for Automatic Lane Change Manoeuvre Considering Traffic Dynamics

This paper proposes a novel algorithm for decision-making on autonomous lane change manoeuvre in vehicles. The proposed approach defines a number of constraints, based on the vehicle’s dynamics and environmental conditions, which must be satisfied for a safe and comfortable lane change manoeuvre. Inclusion of the lateral position of other vehicles on the road and the tyre-road friction are the main advantages of the proposed algorithm. To develop the lane change manoeuvre decision-making algorithm, first, the equations for the lateral movement of the vehicle in terms of manoeuvre time are produced. Then, the critical manoeuvring time is calculated on the basis of the constraints. Finally, the decision is made on the feasibility of carrying out the manoeuvre by comparing the critical times. Numerous simulations, taking into account the tyre-road friction and vehicles’ inertia and velocity, are conducted to compute thecritical times and a model named TUG-LCA is presented based on the corresponding results

Comparative analysis of MPC controllers applied to Autonomous Driving

Este trabajo presenta el diseño de un sistema de evasión de obstáculos, aplicable en situaciones de emergencia. La solución propone un MPC multivariable para controlar la posición, orientación y velocidad del vehículo autónomo. El controlador considera las limitaciones físicas del vehículo, así como la morfología de la vía para conseguir minimizar los posibles daños que puedan afectar al sistema y en consecuencia a la pérdida de control del vehículo. Las restricciones principales están basadas en las fuerzas laterales que afectan a los neumáticos, obtenidas de la implementación de los modelos cinemático y dinámico de la planta. Inicialmente, el controlador hace que el sistema siga una trayectoria predefinida. No obstante, tomará las acciones de evasión necesarias cuando detecte obstáculos, para conseguir realizar trayectorias libres de colisiones. Los resultados obtenidos tras la validación del sistema se presentan con el simulador para conducción autónoma CARLA.This work presents the design of an obstacle avoidance system, employable in emergency situations. The solution proposes a multivariable Model Predictive Controller (MPC) to control the position, orientation and velocity of an autonomous vehicle. The controller considers the vehicle0s physical limitations, as well as the road morphology, to minimize any possible damage to the system and the loss of control of the vehicle. Its main constraints are based on the lateral tire forces, obtained from the implementation of a kinematic and dynamic plant model. The controller, initially following a predefined trajectory, will take the needed evasive actions in order to perform a collision-free trajectory, in case of an obstacle detection. The results obtained from the system validation are presented with CARLA open-source simulator for autonomous driving.Grado en Ingeniería en Electrónica y Automática Industria

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

Human-like motorway lane change trajectory planning for autonomous vehicles.

The human lifestyle can be foreseen to have a tremendous change once the automation of transportation has been fully realised. The majority of current researches merely focus on improving the efficiency performance of autonomous vehicles(e.g. the energy management system, the handling, etc.)instead of putting the human acceptance and preference into consideration, leaving the knowledge gap of achieving the personalised automation. The primary objective of this research is to develop a novel human-like trajectory planning algorithm that is able to mimic the performance of human drivers and generate a feasible trajectory for an autonomous vehicle to complete a motorway lane change, which is the most representative and commonest manoeuvre on the motorway. This thesis can be divided into four main sections. Starting with the part of literature review, which summarises the existing techniques and the associated knowledges that can be taken the advantage of; including the trajectory planning, the driving styles, the lane change manoeuvre and the Model Predictive Control (MPC). An appropriate-designed experiment is then introduced and implemented, with the purpose of constructing a precise and reliable human driving database. This database contains 551 lane changes on the motorway from 12 different male drivers. Through applying data statistics methods, the human characteristics can be mined from the experimental data, showing that the vehicle velocity , the hand steering wheel angle , the longitudinal acceleration a, the rate of hand steering and the rate of longitudinal accelerating are the essential features for the motorway lane change manoeuvre. An off-line constraint table for the three nominated driving styles can be therefore constructed based on these features. Finally, the obtained human information is then fused with the traditional MPC planning technique so as to achieve the proposed human-like trajectory planning algorithm. The main contribution of this study is proposing a novel approach of combining the real human driving data and the traditional planning technique(i.e. MPC) to achieve human-like lane change trajectory planning for autonomous vehicles. An integrated human driving database which contains both the video footages and the vehicle-dynamic-based signals from 12 different participants is built. Moreover, the draft marginal values of the essential parameters for the driving styles while performing a right lane change on the motorway are also presented. Both the collected driving database and the driving styles’ constraint table can be seen as distinctive achievements, providing resourceful materials for future researches.PhD in Transport System