Intelligent vehicle testing has been a hotspot in the field of intelligent vehicles. Due to the multi-dimensional parameters and the continuity of testing scenarios, a complete test of all scenarios requires a large amount of manpower and several material resources. In critical scenarios, deficiencies in an intelligent vehicle’s performance and defects of an algorithm can be exposed. Therefore, increasing the search efficiency and coverage of critical scenarios is key in improving scenario-based intelligent vehicle testing technology. In this study, a critical scenario search method for intelligent vehicle testing based on the social cognitive optimization (SCO) algorithm is proposed. This method has two main parts: global search and local search. The global search, based on the modified SCO algorithm, integrates the density peak clustering (DPC) algorithm and the cooling scheduling function, and aims to find all local aggregation areas of critical scenarios in a logical scenario space. The local search applies a multi-dimensional convolution algorithm to the global search results to find critical scenarios near the local aggregation areas. Finally, a longitudinal automatic driving algorithm is tested using the proposed method under a specified logical scenario in a simulation environment. The test results show that the proposed method can improve both the search efficiency and coverage of critical scenarios. 

A Critical Scenario Search Method for Intelligent Vehicle Testing Based on the Social Cognitive Optimization Algorithm

The Green Light Optimal Speed Advisory (GLOSA) system is a Cooperative Intelligent Transportation System (C-ITS) that is supposed to reduce the energy consumption and travel time associated with a vehicle trip by providing an optimal speed profile to avoid unnecessary stops at an intersection, based on Traffic Light Signals (TLSs) information. It is expected that GLOSA solutions will be widely deployed on transportation networks, thanks to the rapid spread of increasingly effective and pervasive communication technologies. Thus, appropriate methodologies and tools should be adopted to test sustainability objectives and evaluate the effects of such systems on the performance of traffic networks. In this perspective, this paper proposes an enhanced testing approach of GLOSA services based on an Integrated Simulation Environment that combines custom models developed in Matlab/Simulink with the SUMO traffic simulation environment, allowing both the traffic environment and vehicle dynamics to be represented with a suitable level of detail. The proposed approach can be used to cover several aspects which, in the existing technical literature, are: (i) not considered (traffic signal phase condition); (ii) rarely considered (electric engine); (iii) considered in a non-integrated way (traffic condition, TLS cycle duration, communication distance and minimum speed). Indeed, the considered factors pertain to different subsystems of the mobility environment (network and vehicles), and the proposed testing framework allows an equally detailed simulation of all, thus enhancing the accuracy of the results. To show the validity of the proposed approach, this study considers one controlled vehicle, equipped with a GLOSA system, travelling along a single route through a city centre including many TLSs. The simulation analysis deals with different levels of considered factors, assessed through Key Performance Indicators (KPIs) related to mobility and environmental indicators. Numerical results confirm that the proposed Integrated Simulation Environment can give valuable insights and suggestions to design a GLOSA system aiming to enhance both mobility and environmental performance. They also show that the factors in question affect system performance differently. 

An Integrated Simulation Environment to test the effectiveness of GLOSA services under different working conditions

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.

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

Decentralized cooperative crossing at unsignalized intersections via vehicle-to-vehicle communication in mixed traffic flows

Meeting sustainability goals demands rethinking analytical techniques to minimize hazards, waste, and energy use and align with green and white chemistry principles. Herein, we report a novel spectrophotometric chemometrics approach for determining the pain medications lidocaine (LID) and diclofenac (DIC), and the carcinogen impurity 2,6-dimethylaniline (DMA), in ampoules with promising greenness, blueness and whiteness values. Partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR models were built using a multilevel multifactor experimental design resulting in 25 mixtures as a calibration set. A key novelty was using the Latin Hypercube Sampling (LHS) technique to create a robust validation set to evaluate these models' performance and generalization. The GA-PLS model showed the lowest root mean squared error of prediction (0.121, 0.045, and 0.134 for LID, DIC, and DMA respectively), demonstrating its effectiveness. Recovery from pharmaceutical samples was 98–102 %, evidencing the excellent resolution capacity. Comprehensive greenness, blueness, and whiteness assessments using NEMI, GAPI, AGREE, BAGI, and RGB12 tools were performed and compared between the suggested and reported methods, highlighting the importance of holistic sustainability assessment encompassing greenness, analytical merit, and practicality when developing new methods. The proposed strategy proved highly effective, sustainable, affordable, and implementable even in modestly equipped laboratories, aligning with sustainable development goals. Overall, this work pioneers a green-blue-white alternative to conventional approaches that contributes to an eco-friendlier future in quality control practices, serving as a model for developing benign techniques that reduce resource consumption and chemical waste aligning with the global call for environmentally responsible solutions. 

Greenness, blueness and whiteness evaluated-chemometric approach enhanced by Latin hypercube technique for the analysis of lidocaine, diclofenac and carcinogenic impurity 2,6-dimethylaniline

The development of connected and automated driving functions involves that the interaction of autonomous/automated vehicles with the surrounding environment will increase. Accordingly, there is a necessity for an improvement in the usage of traditional tools of the automotive development process. This is a critical problem since the classic development process used in the automotive field uses a very simplified driver model and the traffic environment, while nowadays it should contemplate a realistic representation of these elements. To overcome this issue, the authors proposed an integrated simulation environment, based on the co-simulation of Matlab/Simulink environment with simulation of urban mobility, which allows for a realistic model of vehicle dynamic, control logics, driver behaviour and traffic conditions. Simulation tests have been performed to prove the reasoning for such a tool, and to show the capabilities of the instrument. By using the proposed platform, vehicles may be modelled with a higher level of details (with respect to microscopic simulators), while the autonomous/automated driving functions can be tested in realistic traffic scenarios where the features of the road traffic environment can be varied to verify in a realistic way the level of robustness of the on-board implemented functions.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2019.0678

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

With the spread of automation in driving, vehicles will have to interact more with the surrounding environment, and consequently the automotive development process will have to contemplate a realistic representation of these elements. This is a critical issue, since in the classic development process used in the automotive field, driver model and traffic environment are represented in a very simplified way. This paper overcomes this criticality through the development of an Integrated Simulation Environment, at the Model in the Loop (MIL) level, which allows the integrated representation of the vehicle dynamic and its automation, of the driver and the traffic conditions. The tool is based on integrated simulation of MATLAB / Simulink with the commercial software CarRealTime and with SUMO, an open source microscopic simulator. A series of tests have been performed to prove the need for such a tool, and to show the potential of the instrument. The implemented system allows vehicle, represented with a high level of details, to be tested in realistic traffic scenarios, in which agents and characteristic variables of traffic flow can be varied in order to verify realistically the level of robustness of on-board automation functions.

Motivating the need for an integrated software architecture for Connected and Automated Vehicles technologies development and testing

This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach. 

Assessing safety functionalities in the design and validation of driving automation

Intersections, and more traffic signals, introduce stops along trip increasing travel time, stress, fuel consumption and emissions. To confine the stop-and-go phenomenon due to traffic signals several researchers focused on the use of actuated traffic lights. In last years the digital transformation of cities give us lot of information could be used for several purposes, one of them is the implementation of cooperativeintelligent transportation systems (C-ITS). The paper introduces a specific C-ITS named Green Light Optimal Speed Advisory (GLOSA) that use V2I communication to calculate an optimal speed for the vehicle to pass the intersection avoiding idle time. Two simulations scenarios have been built to study the benefit of the service. The results demonstrate that even using a simple control algorithm with the aim to avoid the stop&go we observe a reduction of fuel consumption and emissions in the region of 5 to 12%

Green Light Optimal Speed Advisory: a C-ITS to improve mobility and pollution

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

Motivating the need for an integrated software architecture for Connected and Automated Vehicles technologies development and testing

Assessing safety functionalities in the design and validation of driving automation

Green Light Optimal Speed Advisory: a C-ITS to improve mobility and pollution