Modeling of Double Lane Change Maneuver of Vehicles

Arefnezhad, Sadegh; Ghaffari, Ali; Khodayari, Alireza; Nosoudi, S

doi:10.1007/s12239-018-0026-z

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…3). The example represents a standard double-lane-change maneuver [43] conducted in two-vehicles configuration, where one engages the Electronic Stability Program (ESP) control while the other switches off such control unit when changing the lane. From this perspective, a simpler scenario was considered by removing Fig.…”

Section: System Descriptionmentioning

confidence: 99%

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

Silano

Iannelli

2021

IEEE Access

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The main motivation of this work is to propose a simulation approach for a specific task within the Unmanned Aerial Vehicle (UAV) field, i.e., the visual detection and tracking of arbitrary moving objects. In particular, it is described MAT-Fly, a numerical simulation platform for multi-rotor aircraft characterized by the ease of use and control development. The platform is based on Matlab ® and the MathWorks ™ Virtual Reality (VR) and Computer Vision System (CVS) toolboxes that work together to simulate the behavior of a quad-rotor while tracking a car that moves along a nontrivial path. The VR toolbox has been chosen due to the familiarity that students have with Matlab and because it does not require a notable effort by the user for the learning and development phase thanks to its simple structure. The overall architecture is quite modular so that each block can be easily replaced with others simplifying the code reuse and the platform customization. Some simple testbeds are presented to show the validity of the approach and how the platform works. The simulator is released as open-source, making it possible to go through any part of the system, and available for educational purposes. INDEX TERMS educational, Matlab/Simulink, image-based visual servoing, trajectory control, vision detection and tracking, software-in-the-loop, unmanned aerial vehicles, multi-rotor

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Section: System Descriptionmentioning

confidence: 99%

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

Silano

Iannelli

2021

IEEE Access

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“…The maneuver chosen for the current study is the emergency double-lane-change maneuver, generally performed on the highway to overtake another vehicle [ 51 ]. The test is commonly adopted because it correlates the ability of controlling the vehicle at the limits of handling with an enhanced safety for the vehicle occupants in scenarios concerning the presence of obstacles on the path [ 52 ].…”

Section: Physical Model Physical Model-based Control and Virtual Scenariomentioning

confidence: 99%

Investigation on the Model-Based Control Performance in Vehicle Safety Critical Scenarios with Varying Tyre Limits

Sakhnevych

Arricale

Bruschetta

et al. 2021

Sensors

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In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to assure the minimum requirements in the worst possible operating conditions instead. Furthermore, their calibration is typically performed in a pre-defined strict range of operating conditions, established by specific regulations or OEM routines. For this reason, their performance can considerably decrease in particularly crucial safetycritical situations, where the environmental conditions (rain, snow, ice), the road singularities (oil stains, puddles, holes), and the tyre thermal and ageing phenomena can deeply affect the adherence potential. The objective of the work is to investigate the possibility of the physical model-based control to take into account the variations in terms of the dynamic behavior of the systems and of the boundary conditions. Different scenarios with specific tyre thermal and wear conditions have been tested on diverse road surfaces validating the designed model predictive control algorithm in a hardware-in-the-loop real-time environment and demonstrating the augmented reliability of an advanced virtual driver aware of available information concerning the tyre dynamic limits. The multidisciplinary proposal will provide a paradigm shift in the development of strategies and a solid breakthrough towards enhanced development of the driving automatization systems, unleashing the potential of physical modeling to the next level of vehicle control, able to exploit and to take into account the multi-physical tyre variations.

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“…The authors of [32] formulates the problem as a series of convex approximation, and an optimal trajectory that minimizes yaw acceleration is calculated and drives through it with an MPC controller. In [33], the authors use black-box modeling of double lanechange maneuver based on Adaptive Neuro-Fuzzy System (ANFIS) and presents a simplified test with an actual vehicle at low speed (30 km/h). In [34], the authors present an explicit MPC controller to reduce the enormous computational complexity of MPC by using an mp-QP technique and validates it in the CarSim simulator.…”

Section: A Related Workmentioning

confidence: 99%

Hierarchical Evasive Path Planning Using Reinforcement Learning and Model Predictive Control

2020

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Motion planning plays an essential role in designing self-driving functions for connected and autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to follow, fulfilling different requirements, such as safety, efficiency, and passenger comfort. In this area, algorithms must also meet strict real-time expectations, since, especially in an emergency, the decision time is limited, which raises a trade-off for the feasibility requirements. This article proposes a hierarchical path planning solution for evasive maneuvering, where a Twin Delayed DDPG reinforcement learning agent generates the parameters of a geometric path consisting of chlotoids and straight sections, and an underlying model predictive control loop fulfills the trajectory following tasks. The method is applied to the automotive double lane-change test, a common emergency situation, comparing its results with human drivers' performance using a dynamic simulation environment. Besides the test's standardized parameters, a broader range of topological layouts is chosen, both for the training and performance evaluation. The results show that the proposed method highly outperforms human drivers, especially in challenging situations, while meeting the computational requirements, as the pre-trained neural network and path generation algorithm can provide a solution in an instant, based on the experience gained during the training process.

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Modeling of Double Lane Change Maneuver of Vehicles

Cited by 14 publications

References 15 publications

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

Investigation on the Model-Based Control Performance in Vehicle Safety Critical Scenarios with Varying Tyre Limits

Hierarchical Evasive Path Planning Using Reinforcement Learning and Model Predictive Control

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