Integrating tools for an effective testing of connected and automated vehicles technologies

Pariota, Luigi; Coppola, Angelo; Costanzo, Luca Di; Vico, Antonio Di; Andolfi, Arcangelo; D'Aniello, Claudio; Bifulco, Gennaro Nicola

doi:10.1049/iet-its.2019.0678

Cited by 19 publications

(5 citation statements)

References 40 publications

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“…MIL simulation tools for effective testing of connected and automated driving technologies are presented in [28]. Other actors on the road are added using co-simulation of a microscopic traffic simulator such as SUMO in MIL simulation [15,28]., Worst-case scenario analysis and accelerated analysis can be performed using the many available AV MIL simulation programs for focusing on rare and extreme events [29]. While MIL simulation is very useful, the low-and high-level control systems, the dynamics of the host vehicle and other significant road actors are modeled using software.…”

Section: Related Workmentioning

confidence: 99%

“…Certification agencies and local governments will be able to use this tool to fully test vendor technologies before certification and for accident reconstruction and analysis. [15,20,[28][29][30][31][32][33]39] hard [20,33,39] hard [20,33,37] hard [20,25] hard/soft [38] Adaptability to Different Scenarios relatively easy [15,20,[28][29][30][31][32][33]39] difficult [20,33,39] very difficult [20,33] not possible [20,25,26] easy [38] Vehicle Model high fidelity [15,20,[28][29][30][31][32][33]39] real vehicle [20,33,39] real vehicle ...…”

Section: Proposed Approachmentioning

confidence: 99%

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Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration

Xiao

Chen

Gelbal

et al. 2023

Sensors

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The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these connected and autonomous driving functions in this approach. This is an unsafe, costly and inefficient method. Motivated by these shortcomings, this paper introduces the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method is compared to the existing state-of-the-art. Its basic implementation for a path-following task is used to explain the method where the actual autonomous vehicle operates in a large empty area with its sensor feeds being replaced by realistic sensor feeds corresponding to its location and pose in the virtual environment. It is possible to easily change the development virtual environment and inject rare and difficult events which can be tested very safely. Vehicle-to-Pedestrian (V2P) communication-based pedestrian safety is chosen as the application use case for the VVE in this paper, and corresponding experimental results are presented and discussed. A no-line-of-sight pedestrian and vehicle moving towards each other on intersecting paths with different speeds are used in the experiments. Their time-to-collision risk zone values are compared for determining severity levels. The severity levels are used to slow down or brake the vehicle. The results show that V2P communication of pedestrian location and heading can be used successfully to avoid possible collisions. It is noted that actual pedestrians and other vulnerable road users can be used very safely in this approach.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration

Xiao

Chen

Gelbal

et al. 2023

Sensors

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“…The ndings of this research showed that considering the integration of vehicle dynamics and tra c microsimulation models in emission estimation leads to more reliable results compared to the microsimulation-only approach. In addition, to model the behavior of autonomous and connected vehicles more realistic, Pariota et al also implemented vehicle dynamics models besides the tra c microsimulation model simulated in SUMO regardless of vehicle emissions (Pariota et al, 2020). Their results also con rmed the reliability, functionality and added value of using vehicle dynamic model..…”

Section: System Level 331 Vehicle System Dynamics Modelmentioning

confidence: 99%

A Novel Approach for Brake Emission Estimation based on Traffic Microsimulation, Vehicle System Dynamics, and Machine Learning Modeling

Rahimi

Bortoluzzi

Candeo

et al. 2022

Preprint

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Brake wear is known as the primary source of traffic-related non-exhaust particle generation. Its generation rate is influenced by parameters at different levels: subsystem (type of brakes, pads, materials, etc.), system (vehicles’ dynamics, driving style etc.) and suprasystem (road geometries, traffic parameters, etc.). At the subsystem level, we proposed a neural network brake emission modeling, trained and validated through emission data collected from a reduced-scale dynamometer. At the system level, a model of a car dynamics was developed to calculate the wheels’ brake torques and angular velocities. At the suprasystem level, the traffic behavior in a sensitive urban area was characterized experimentally and simulated in a traffic microsimulation software. The vehicle traffic-based records were used to calculate the vehicle dynamic quantities, converted into brake emission through the neural network. To examine the overall traffic impacts on brake emission, the total number of brake wear particles was estimated regarding the route choice in the sensitive area and in the whole transportation network. The findings of this study showed significant impacts of brake wear on air pollution. The ground-truth brake emission estimation in a real area provides fundamental information to the decision-makers to better insight into the rate of non-exhaust emissions generation.

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“…The longitudinal behaviour of each EV i (∀i = 1, • • • N) can be depicted by the following nonlinear dynamics [34][35][36]:…”

Section: Nonlinear Longitudinal Ev Modelmentioning

confidence: 99%

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

et al. 2021

Self Cite

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This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

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Integrating tools for an effective testing of connected and automated vehicles technologies

Cited by 19 publications

References 40 publications

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration

A Novel Approach for Brake Emission Estimation based on Traffic Microsimulation, Vehicle System Dynamics, and Machine Learning Modeling

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

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