Multiform Logical Time & Space for Mobile Cyber-Physical System with Automated Driving Assistance System

International audienceWe study the use of Multiform Logical Time, as embodied in Esterel/SyncCharts and Clock Constraint Specification Language (CCSL), for the specification of assume-guarantee constraints providing safe driving rules related to time and space, in the context of Automated Driving Assistance Systems (ADAS). The main novelty lies in the use of logical clocks to represent the epochs of specific area encounters (when particular area trajectories just start overlapping for instance), thereby combining time and space constraints by CCSL to build safe driving rules specification. We propose the safe specification pattern at high-level that provide the required expressiveness for safe driving rules specification. In the pattern, multiform logical time provides the power of parameterization to express safe driving rules, before instantiation in further simulation contexts. We present an efficient way to irregularly update the constraints in the specification due to the context changes, where elements (other cars, road sections, traffic signs) may dynamically enter and exit the scene. In this way, we add constraints for the new elements and remove the constraints related to the disappearing elements rather than rebuild everything. The multi-lane highway scenario is used to illustrate how to irregularly and efficiently update the constraints in the specification while receiving a fresh scene

No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles

Defined traffic laws must be respected by all vehicles. However, it is essential to know which behaviors violate the current laws, especially when a responsibility issue is involved in an accident. This brings challenges of digitizing human-driver-oriented traffic laws and monitoring vehicles' behaviors continuously. To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles. This paper introduces a layered trigger domain-based traffic law digitization architecture with digitization-classified discussions and detailed atomic propositions for online monitoring. The principal laws on a highway and at an intersection are taken as examples, and the corresponding logic and atomic propositions are introduced in detail. Finally, the digitized traffic laws are verified on the Chinese highway and intersection datasets, and defined thresholds are further discussed according to the driving behaviors in the considered dataset. This study can help manufacturers and the government in defining specifications and laws and can also be used as a useful reference in traffic laws compliance decision-making. Source code is available on https://github.com/SOTIF-AVLab/DOTL.Comment: 22 pages, 11 figure

Legal Decision-making for Highway Automated Driving

Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even result in accidents in mixed traffic flow. Therefore, ensuring the compliance of the autonomous driving decision-making system is essential for ensuring the safety of autonomous driving and promoting the widespread adoption of autonomous driving technology. To this end, the paper proposes a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliantly trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing. The results indicate that the proposed method increases the vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the proportion of active violations to 0%, demonstrating its effectiveness.Comment: 14 pages, 17 figure

Operational Design Domain Monitoring and Augmentation for Autonomous Driving

Recent technological advances in Autonomous Driving Systems (ADS) show promise in increasing traffic safety. One of the critical challenges in developing ADS with higher levels of driving automation is to derive safety requirements for its components and monitor the system's performance to ensure safe operation. The Operational Design Domain (ODD) for ADS confines ADS safety to the context of its function. The ODD represents the operating environment within which an ADS operates and satisfies the safety requirements. To reach a state of "informed safety", the system's ODD must be explored and well-tested in the development phase. At the same time, the ADS must monitor the operating conditions and corresponding risks in real-time. Existing research and technologies do not directly express the ODD quantitatively, nor do they have a general monitoring strategy designed to handle the learning-based system, which is heavily used in the recent ADS technologies. The safety-critical nature of the ADS requires us to provide thorough validation, continual improvement, and safety monitoring of these data-driven dependent modules. In this dissertation, the ODD extraction, augmentation, and real-time monitoring of the ADS with machine learning components are investigated. There are three major components for the ODD of the ADS with machine learning components for general safety issues. In the first part, we propose a framework to systematically specify and extract the ODD, including the environment modeling and formal and quantitative safety specifications for models with machine learning parts. An empirical demonstration of the ODD extraction process based on predefined specifications is presented with the proposed environment model. In the second part, the ODD augmentation in the development phase is modelled as an iterative engineering problem solved by robust learning to handle unseen future natural variations. The vision tasks in ADS are the major focus here, and the effectiveness of model-based robustness training is demonstrated, which can improve model performance and the application of extracting edge cases during the iterative process. Furthermore, the testing procedure provides us with valuable priors on the probability of failures in the known testing environment, which can be further utilized in the real-time monitoring procedure. Finally, a solution for online ODD monitoring that utilizes the knowledge from the offline validation process as Bayesian graphical models to improve safety warning accuracy is provided. While the algorithms and techniques proposed in this dissertation can be applied to many safety-critical robotic systems with machine learning components, in this dissertation the main focus lies on the implications for autonomous driving

Definition of the 2005 flight deck environment

A detailed description of the functional requirements necessary to complete any normal commercial flight or to handle any plausible abnormal situation is provided. This analysis is enhanced with an examination of possible future developments and constraints in the areas of air traffic organization and flight deck technologies (including new devices and procedures) which may influence the design of 2005 flight decks. This study includes a discussion on the importance of a systematic approach to identifying and solving flight deck information management issues, and a description of how the present work can be utilized as part of this approach. While the intent of this study was to investigate issues surrounding information management in 2005-era supersonic commercial transports, this document may be applicable to any research endeavor related to future flight deck system design in either supersonic or subsonic airplane development

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio