Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections

The aim of the paper is to describe a multicriteria model predictive control method for autonomous vehicles at non-signalized intersections. The centralized controller aims to describe control action for each autonomous vehicle to guarantee collision free passage. At the same time, performances are defined for the centralized Model Predictive Controller, namely the minimization of traveling time and energy consumption. Since these control goals are often conflicting, a scheduling variable is introduced to create a balance between them. Hence, the centralized controller can be tuned based on the importance of each control goal, which can be useful in urban environment where traffic densities may vary heavily depending on the period of the day. The effectiveness of the proposed centralized multicriteria controller is demonstrated through a complex simulation example in CarSim simulation environment using different tpye of autonomous vehicles.

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The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211009084 ◽

2021 ◽

pp. 095440702110090

Author(s):

Xing Xu ◽

Minglei Li ◽

Feng Wang ◽

Ju Xie ◽

Xiaohan Wu ◽

...

Keyword(s):

Optimal Control ◽

Autonomous Vehicles ◽

Optimal Trajectory ◽

Control Method ◽

Autonomous Vehicle ◽

Trajectory Data ◽

Test Track ◽

Optimal Control Method ◽

The Future ◽

On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

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An Open-Source Scale Model Platform for Teaching Autonomous Vehicle Technologies

Sensors ◽

10.3390/s21113850 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3850

Author(s):

Bastien Vincke ◽

Sergio Rodriguez Rodriguez Florez ◽

Pascal Aubert

Keyword(s):

Open Source ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Constrained Systems ◽

Scale Model ◽

Technical Students ◽

Vehicle Technologies ◽

Model Platform ◽

High Level ◽

Time And Energy

Emerging technologies in the context of Autonomous Vehicles (AV) have drastically evolved the industry’s qualification requirements. AVs incorporate complex perception and control systems. Teaching the associated skills that are necessary for the analysis of such systems becomes a very difficult process and existing solutions do not facilitate learning. In this study, our efforts are devoted to proposingan open-source scale model vehicle platform that is designed for teaching the fundamental concepts of autonomous vehicles technologies that are adapted to undergraduate and technical students. The proposed platform is as realistic as possible in order to present and address all of the fundamental concepts that are associated with AV. It includes all on-board components of a stand-alone system, including low and high level functions. Such functionalities are detailed and a proof of concept prototype is presented. A set of experiments is carried out, and the results obtained using this prototype validate the usability of the model for the analysis of time- and energy-constrained systems, as well as distributed embedded perception systems.

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Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction

Sensors ◽

10.3390/s20041079 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1079 ◽

Cited By ~ 1

Author(s):

Fen Lin ◽

Kaizheng Wang ◽

Youqun Zhao ◽

Shaobo Wang

Keyword(s):

Autonomous Vehicles ◽

Control Method ◽

Integrated Control ◽

Autonomous Vehicle ◽

Longitudinal Velocity ◽

Reference Trajectory ◽

Threshold Method ◽

Lane Changing ◽

Information Interaction ◽

Fitting In

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability.

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The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

Applied Sciences ◽

10.3390/app11083464 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3464

Author(s):

Balázs Németh ◽

Péter Gáspár

Keyword(s):

Collision Avoidance ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Signalized Intersections ◽

Signalized Intersection ◽

Optimal Motion ◽

Unsignalized Intersections ◽

Environment Model ◽

Safety Constraints ◽

Constrained Quadratic Optimization

The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.

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Driving and steering collision avoidance system of autonomous vehicle with model predictive control based on non-convex optimization

Advances in Mechanical Engineering ◽

10.1177/16878140211027669 ◽

2021 ◽

Vol 13 (6) ◽

pp. 168781402110276

Author(s):

Yuho Song ◽

Kunsoo Huh

Keyword(s):

Convex Optimization ◽

Collision Avoidance ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Lane Changes ◽

Vehicle Communication ◽

Rapid Control ◽

Rapid Control Prototyping ◽

Predictive Controller ◽

Collision Avoidance System

A planar motion control system is proposed for autonomous vehicles not only to follow the lanes, but also to avoid collisions by braking, accelerating, and steering. The supervisor is designed first to determine the desired speed and the risk of the maneuvering due to road boundaries and obstacles. In order to allow lane changes on multi-lane roads, the model predictive controller is formulated based on the probabilistic non-convex optimization. The micro-genetic algorithm is applied to calculate the target speed and target steering angle in real time. A software-in-the-loop unit is constructed with the Rapid Control Prototyping device in the vehicle communication environment. The performance of the proposed system is verified for various collision avoidance scenarios and the simulation results demonstrate the safe and effective driving performance of autonomous vehicles with no collision on multi-lane road.

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Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119855606 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 494-507 ◽

Cited By ~ 1

Author(s):

Mhafuzul Islam ◽

Mashrur Chowdhury ◽

Hongda Li ◽

Hongxin Hu

Keyword(s):

Object Detection ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Semantic Segmentation ◽

Steering Wheel ◽

Potential Hazard ◽

Driving System ◽

Hazardous Object ◽

Vision Based Navigation ◽

Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽

10.3390/s21062244 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2244

Author(s):

S. M. Yang ◽

Y. A. Lin

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Optimal Path ◽

Random Trees ◽

Average Deviation ◽

Lane Changes ◽

Path Smoothing ◽

Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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Modelling of the system “driver - automation - autonomous vehicle - road”

Open Engineering ◽

10.1515/eng-2020-0016 ◽

2020 ◽

Vol 10 (1) ◽

pp. 175-182 ◽

Cited By ~ 1

Author(s):

Grzegorz Koralewski

Keyword(s):

Autonomous Vehicles ◽

Combustion Engine ◽

Autonomous Vehicle ◽

Application Software ◽

Motion Simulation ◽

Gear Shift ◽

Shift Control ◽

Driving Torque ◽

Physical Quantities ◽

Motion Quality

AbstractThe work presents a simulation model of a “driver–automation–autonomous vehicles–road” system which is the basis for synthesis of automatic gear shift control system. The mathematical description makes use of physical quantities which characterise driving torque transformation from the combustion engine to the car driven wheels. The basic components of the model are algorithms for the driver’s action logic in controlling motion velocity, logic of gear shift control functioning regarding direction and moment of switching, for determining right-hand side of differential equations and for motion quality indicators. The model is realised in a form of an application software package, comprising sub-programmes for input data, for computerised motion simulation of cars with mechanical and hydro-mechanical – automatically controlled – transmission systems and for models of characteristic car routes.

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Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽

10.3390/en14123425 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3425

Author(s):

Huanping Li ◽

Jian Wang ◽

Guopeng Bai ◽

Xiaowei Hu

Keyword(s):

Traffic Flow ◽

Autonomous Vehicles ◽

Flow Model ◽

Road Traffic ◽

Autonomous Vehicle ◽

Transformation Model ◽

Hydrodynamic Theory ◽

Traffic Flow Model ◽

Traffic System ◽

The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

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Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

Sensors ◽

10.3390/s21010297 ◽

2021 ◽

Vol 21 (1) ◽

pp. 297

Author(s):

Ali Marzoughi ◽

Andrey V. Savkin

Keyword(s):

Motion Control ◽

Autonomous Vehicles ◽

Autonomous Navigation ◽

Control Algorithm ◽

Autonomous Vehicle ◽

Sufficient Conditions ◽

Real Life ◽

Unmanned Surface Vehicles ◽

Necessary And Sufficient ◽

See Region

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

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