Showing papers on "Vehicle dynamics published in 2015"

Potential Cyberattacks on Automated Vehicles

Abstract: Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, we investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. We analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting. We also raise awareness to generate discussion about these threats at this early stage in the development of vehicle automation systems.

Distributed Consensus Strategy for Platooning of Vehicles in the Presence of Time-Varying Heterogeneous Communication Delays

Abstract: We analyze and solve the platooning problem by treating it as the problem of achieving consensus in a network of dynamical systems affected by time-varying heterogeneous delays due to wireless communication among vehicles Specifically, a platoon is modeled as a dynamical network where: 1) each vehicle, with its own dynamics, is a node; 2) the presence of communication links between neighboring vehicles is represented by edges; and 3) the structure of the intervehicle communication is encoded in the network topology A distributed control protocol, which acts on every vehicle in the platoon, is derived It is composed of two terms: a local action depending on the state variables of the vehicle itself (measured onboard) and an action depending on the information received from neighboring vehicles through the communication network The stability of the platoon is proven by using Lyapunov–Razumikhin theorem Numerical results are included to confirm and illustrate the theoretical derivation

Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle

Abstract: This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

Robust Control of Four-Rotor Unmanned Aerial Vehicle With Disturbance Uncertainty

Abstract: This paper addresses the stability and tracking control problem of a quadrotor unmanned flying robot vehicle in the presence of modeling error and disturbance uncertainty. The input algorithms are designed for autonomous flight control with the help of an energy function. Adaptation laws are designed to learn and compensate the modeling error and external disturbance uncertainties. Lyapunov theorem shows that the proposed algorithms can guarantee asymptotic stability and tracking of the linear and angular motion of a quadrotor vehicle. Compared with the existing results, the proposed adaptive algorithm does not require an a priori known bound of the modeling errors and disturbance uncertainty. To illustrate the theoretical argument, experimental results on a commercial quadrotor vehicle are presented.

Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles

Abstract: As the need for autonomous reconnaissance and surveillance missions in cluttered urban environments has been increasing, this paper describes a cooperative path planning algorithm for tracking a moving target in urban environments using both unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). The novelty of the algorithm is that it takes into account vision occlusions due to obstacles in the environment. The algorithm uses a dynamic occupancy grid to model the target state, which is updated by sensor measurements using a Bayesian filter. Based on the current and predicted target behavior, the path planning algorithm for a single vehicle (UAV/UGV) is first designed to maximize the sum of the probability of detection over a finite look-ahead horizon. The algorithm is then extended to multiple vehicle collaboration scenarios, where a decentralized planning algorithm relying on an auction scheme is designed to plan finite look-ahead paths that maximize the sum of the joint probability of detection over all vehicles.

Tracking differentiator design for the robust backstepping control of a flexible air-breathing hypersonic vehicle

Abstract: This paper is concerned with the robust backstepping controller design for a flexible air-breathing hypersonic vehicle. Due to the extreme complexity of the vehicle dynamics, only the longitudinal model is adopted and rewritten as a feedback form for the backstepping design. Then, a new tracking differentiator (TD) is designed based on hyperbolic sine function to solve the problem of “explosion of term” in the traditional backstepping control. Furthermore, to enhance the controller׳s robustness, a new nonlinear disturbance observer is constructed using the proposed TD to estimate the model uncertainties and varying disturbances. More specially, owing to the measurement difficulties of angle of attack and flight-path angle in practice, the developed TD is utilized to reconstruct them based on the measurable states. Finally, several numerical simulations are given to demonstrate the effectiveness of the proposed control strategy.

Eco-Departure of Connected Vehicles With V2X Communication at Signalized Intersections

Abstract: Eco-driving at signalized intersections has significant potential for energy saving. In this paper, we focus on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission. A Bolza-type optimal control problem (OCP) is formulated to minimize engine fuel consumption. Due to the discrete gear ratio, this OCP is a nonlinear mixed-integer problem, which is challenging to handle by most existing optimization methods. The Legendre pseudospectral method combining the knotting technique is employed to convert it into a multistage interconnected nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The fuel-saving benefit of the optimized eco-departing operation is validated by a passenger car with a five-speed transmission. For real-time implementation, a near-optimal departing strategy is proposed to quickly determine the behavior of the engine and transmission. When a string of vehicles are departing from an intersection, the acceleration of the leading vehicle(s) should be considered to control the following vehicles. This issue is also addressed in this paper.

A Globally Stabilizing Path Following Controller for Rotorcraft With Wind Disturbance Rejection

Abstract: This brief addresses the design and experimental evaluation of a global controller to steer a quadrotor vehicle along a predefined path in the presence of constant wind disturbances. The proposed solution consists of a nonlinear adaptive state feedback controller for thrust and torque actuation that: 1) guarantees global convergence of the closed-loop path following error to zero in the presence of constant wind disturbances and 2) ensures that the actuation can be bounded as a function of the position and velocity errors without imposing a maximum for that bound, allowing for high performance control action. A prototyping and testing architecture, developed to streamline the implementation and tuning of the controller, is also described. Simulation results and experimental results, which include a hovering flight in the slipstream of a mechanical fan, are presented to assess the performance and robustness of the proposed controller.

Integral Sliding Mode for the Torque-Vectoring Control of Fully Electric Vehicles: Theoretical Design and Experimental Assessment

Abstract: This paper presents an integral sliding mode (ISM) formulation for the torque-vectoring (TV) control of a fully electric vehicle. The performance of the controller is evaluated in steady-state and transient conditions, including the analysis of the controller performance degradation due to its real-world implementation. This potential issue, which is typical of sliding mode formulations, relates to the actuation delays caused by the drivetrain hardware configuration, signal discretization, and vehicle communication buses, which can provoke chattering and irregular control action. The controller is experimentally assessed on a prototype electric vehicle demonstrator under the worst-case conditions in terms of drivetrain layout and communication delays. The results show a significant enhancement of the controlled vehicle performance during all maneuvers.

An optimal torque distribution control strategy for four-independent wheel drive electric vehicles

Abstract: In this paper, an optimal torque distribution approach is proposed for electric vehicle equipped with four independent wheel motors to improve vehicle handling and stability performance. A novel objective function is formulated which works in a multifunctional way by considering the interference among different performance indices: forces and moment errors at the centre of gravity of the vehicle, actuator control efforts and tyre workload usage. To adapt different driving conditions, a weighting factors tuning scheme is designed to adjust the relative weight of each performance in the objective function. The effectiveness of the proposed optimal torque distribution is evaluated by simulations with CarSim and Matlab/Simulink. The simulation results under different driving scenarios indicate that the proposed control strategy can effectively improve the vehicle handling and stability even in slippery road conditions.

How Shadowing Hurts Vehicular Communications and How Dynamic Beaconing Can Help

Abstract: We study the effect of radio signal shadowing dynamics, caused by vehicles and by buildings, on the performance of beaconing protocols in Inter-Vehicular Communication (IVC). Recent research indicates that beaconing, i.e., one hop message broadcast, shows excellent characteristics and can outperform other communication approaches for both safety and efficiency applications, which require low latency and wide area information dissemination, respectively. To mitigate the broadcast storm problem, adaptive beaconing solutions have been proposed and designed. We show how shadowing dynamics of moving obstacles hurt IVC, reducing the performance of beaconing protocols. To the best of our knowledge, this is one of the first studies on identifying the problem and the underlying challenges and proposing the opportunities presented by such challenges. Shadowing also limits the risk of overloading the wireless channel. We demonstrate how these challenges and opportunities can be taken into account and outline a novel approach to dynamic beaconing. It provides low-latency communication (i.e., very short beaconing intervals), while ensuring not to overload the wireless channel. The presented simulation results substantiate our theoretical considerations.

Should the Desired Heading in Path Following of Autonomous Vehicles be the Tangent Direction of the Desired Path

Abstract: The path-following problem for autonomous vehicles is investigated in this paper. The desired vehicle heading is commonly chosen as the tangent direction on the desired path. This paper points out that the traditional definition of the desired heading may deteriorate the path-following performance, particularly when the vehicle is tracking a path with large curvature. That is because the sideslip angle control and the yaw rate control are conflicting in the presence of sliding effects, and the sideslip angle does not equal to zero when the vehicle is tracking a curve path. This paper further provides an amendment to the definition of the desired heading, which realizes a more accurate path-following maneuver. In the controller design phase, backstepping is used to generate the required yaw rate, and an LQR controller is adopted to obtain the optimal active front steering input. The CarSim–Simulink joint simulation verifies the reasonability of the amendment to the desired heading.

A novel vehicle dynamics stability control algorithm based on the hierarchical strategy with constrain of nonlinear tyre forces

Abstract: Direct yaw moment control (DYC), which differentially brakes the wheels to produce a yaw moment for the vehicle stability in a steering process, is an important part of electric stability control system In this field, most control methods utilise the active brake pressure with a feedback controller to adjust the braked wheel However, the method might lead to a control delay or overshoot because of the lack of a quantitative project relationship between target values from the upper stability controller to the lower pressure controller Meanwhile, the stability controller usually ignores the implementing ability of the tyre forces, which might be restrained by the combined-slip dynamics of the tyre Therefore, a novel control algorithm of DYC based on the hierarchical control strategy is brought forward in this paper As for the upper controller, a correctional linear quadratic regulator, which not only contains feedback control but also contains feed forward control, is introduced to deduce the object of

Apparatus and method for continuously establishing a boundary for autonomous driving availability and an automotive vehicle comprising such an apparatus

Abstract: Provided are a method and apparatus for continuously establishing a boundary for autonomous driving availability, in a vehicle having autonomous driving capabilities and comprising at least one remote sensor for acquiring vehicle surrounding information and at least one vehicle dynamics sensor for determining vehicle dynamics parameters. The method and apparatus include at least one of a positioning arrangement that provides map data with associated information, a route planning arrangement that enables route planning, a vehicle driver monitoring arrangement that provides driver monitoring information, and a real time information acquiring arrangement that acquires at least one of traffic information and weather information. The boundary is calculated based on a planned route and at least one of vehicle surrounding information, vehicle dynamics parameters, driver monitoring information, map data, traffic information and weather information, for the planned route. Changes in the calculated boundary are output to a human machine interface in the vehicle.

Coordinated Adaptive Cruise Control System With Lane-Change Assistance

Abstract: To address the problem caused by a conventional adaptive cruise control (ACC) system, which hinders drivers from changing lanes, in this study we propose a novel coordinated ACC system with a lane-change assistance function, which enables dual-target tracking, safe lane change, and longitudinal ride comfort We first analyze lane-change risk by calculating minimum safety spacing between the host vehicle and surrounding vehicles and then develop a coordinated control algorithm using model predictive control theory Tracking performance is designed on the basis of tracking errors of the host car and two leading vehicles, safety performance is realized by considering the safe distance between the host car and surrounding vehicles, and ride comfort performance is realized by limiting the vehicle's longitudinal acceleration Driver-in-the-loop tests performed on a driving simulator confirm that the proposed ACC system can overcome the disadvantages of conventional ACC and achieves multiobjective coordination in the lane-change process

On the neuro-adaptive feedback linearising control of underactuated autonomous underwater vehicles in three-dimensional space

Abstract: This study addresses the input-output feedback linearisation and the internal dynamics stability of an underactuated autonomous underwater vehicle (AUV) in three-dimensional space. By taking the coordinates of a virtual reference point in front of AUV system as the output equation, the input-output feedback linearisability of AUV kinematics and dynamics is guaranteed. A non-linear controller is designed to make the reference point track a desired trajectory which is generated by an open-loop path planner. Then, it is shown that the resulting internal dynamics of the system is stable. Neural network approximation capabilities and adaptive techniques are also adopted to compensate for unknown vehicle parameters, and constant or time-varying disturbances induced by waves and ocean currents. A Lyapunov-based stability analysis is used to show uniform ultimate boundedness of tracking errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed control system as a qualified candidate for real implementations in offshore applications.

Adaptive Rigidity-Based Formation Control for Multirobotic Vehicles With Dynamics

Abstract: In this brief, we introduce a graph rigidity-based, adaptive formation control law for multiple robotic vehicles moving on the plane that explicitly accounts for the vehicle dynamics while allowing for parametric uncertainty. We consider a class of vehicles modeled by Euler-Lagrange-like equations of motion. The control is designed via backstepping, and exploits rigid graph theory and the structural properties of the system dynamics. A Lyapunov analysis shows that the desired formation is acquired asymptotically. A five-vehicle simulation is used to illustrate the proposed formation acquisition control.

Drive Analysis Using Vehicle Dynamics and Vision-Based Lane Semantics

Abstract: Naturalistic driving studies (NDSs) capture large volumes of drive data from multiple sensor modalities, which are analyzed for critical information about driver behavior and driving characteristics that lead to crashes and near crashes. One of the key steps in such studies is data reduction, which is defined as a process by which “trained employees” review segments of driving video and record a taxonomy of variables that provides information regarding the sequence of events leading to crashes. Given the volume of sensor data in NDSs, such manual analysis of the drive data can be time-consuming. In this paper, we introduce “drive analysis” as one of the first steps toward automating the process of extracting midlevel semantic information from raw sensor data. Techniques are proposed to analyze the sensor data from multiple modalities and to extract a set of 23 semantics about lane positions, vehicle localization within lanes, vehicle speed, traffic density, and road curvature. The proposed techniques are demonstrated using real-world test drives comprising over 150 000 frames of visual data, which are also accompanied by vehicle dynamics that are captured from an in-vehicle controller-area-network bus, an inertial motion unit, and a Global Positioning System.

Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Abstract: This paper presents a reinforcement learning (RL)–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming–based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.

A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter

Abstract: This paper proposes a novel algorithm to identify three inertial parameters: sprung mass, yaw moment of inertia, and longitudinal position of the center of gravity. A four-wheel nonlinear vehicle model with roll dynamics and a correlation between the inertial parameters is used for a dual unscented Kalman filter to simultaneously identify the inertial parameters and the vehicle state. A local observability analysis on the nonlinear vehicle model is used to activate and deactivate different modes of the proposed algorithm. Extensive CarSim simulations and experimental tests show the performance and robustness of the proposed approach on a flat road with a constant tire–road friction coefficient.

Estimation of lateral tire–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach

Abstract: Vehicle control systems require certain vehicle information (e.g., tire–road forces and vehicle sideslip angle) concerning vehicle-dynamic parameters and vehicle–road interaction, which is difficult to measure directly for both technical and economedic reasons. This paper proposes a novel method to estimate lateral tire–road forces and vehicle sideslip angle by utilizing real-time measurements. The estimation method is based on an interacting multiple model (IMM) filter that integrates in-vehicle sensors of in-wheel-motor-driven electric vehicles to adapt multiple vehicle–road system models to variable driving conditions. Based on a four-wheel nonlinear vehicle dynamics model (NVDM) considering extended roll dynamics and load transfer, the vehicle–road system model set of the IMM filter is consists of a linear tire model based NVDM and a nonlinear Dugoff tire model based NVDM. Therefore, the IMM filter can integrate the estimates from two kinds of different vehicle–road system models to improve estimation accuracy. To address system nonlinearities and un-modeled dynamics, the interacting multiple model-unscented Kalman filter (IMM-UKF) and the interacting multiple model-extended Kalman filter (IMM-EKF) are investigated and compared simultaneously. Simulation using Matlab/Simulink-Carsim is carried out to verify the effectiveness of the proposed estimation methods. The results show that the developed estimation methods can accurately estimate lateral tire–road forces and the vehicle sideslip angle.