Driving autonomously in diverse environments remains a significant challenge, especially when transitioning between regions with distinct traffic cultures and regulations. While human drivers exhibit remarkable adaptability through experiential learning and cognitive modeling, current data-driven autonomous systems often struggle with environmental adaptation, interpretability, and continuous learning capabilities. In this work, we present LoDriver (Local Driver), a novel knowledge-based architecture that enhances the conventional scene understanding-decision-planning paradigm through cognitive-inspired dual-process reasoning for path planning. LoDriver integrates a reactive process and a deliberative mechanism, processing multi-modal scene descriptions through parallel pathways. It maintains a structured memory module that dynamically accumulates driving experiences, traffic regulations, and knowledge, enabling experience-based decision-making and continuous learning through systematic memory updates. Experimental evaluation on the nuScenes dataset demonstrates LoDriver's interpretability and enhanced performance compared to existing knowledge-driven models, highlighting its advantage in operating across different environments.

This study addresses the critical need for refined, reliable, and complete real-world trajectory data in the de-velopment of Advanced Driver Assistance Systems (ADAS), particularly for Adaptive Cruise Control (ACC) functions. We conducted a comprehensive comparison of car-following and deceleration scenarios across ten open-source datasets from multiple countries, encompassing both highway and urban environments. Focusing on key kinematic variables crucial for longitudinal behavior, we employed statistical measures and safety metrics to compare data sets across different driving regulations and road designs. Our findings reveal substantial overlaps in the distributions of logical parameters, despite the varied data sources and cultural contexts. However, we noted significant differences in safety-critical metrics, such as Time Headway and Time To Collision (TTC), highlighting culture-specific driving behaviors. Interestingly, Chinese datasets consistently exhibited the smallest distance head ways across all scenarios, yet maintained high TTC values (around 16s) compared to other datasets, suggesting a unique approach to risk management. To quantify these differences, we calibrated the Intelligent Driver Model using U.S. data and evaluated its transferability, demonstrating remarkable performance degradation when applied to non-U.S. datasets. These results provide crucial insights for developing globally applicable, yet culturally sensitive safety assessment methodologies for next-generation automated vehicles, highlighting the need for adaptive ADAS technologies that can accommodate regional driving norms while maintaining consistent safety standards. The code and extracted Longitudinal Trajectory data used in this study are available: https://github.com/imanetaourarti/Car-Following-analysis.

Automotive E/E architectures truly became a creative ground for designers to innovate. Yet, we face a glass-ceiling trying to exploit such potential with existing tools, which led produced heterogeneous architectures to be altered over and over again. However, the subject remains neither simple nor in the hand of a single actor: carmakers, OEMs and standard consortiums do share responsibilities for those ambitious architectures. We developed a more meta oriented methodology, illustrated with us integrating an abstracted driving monitoring system (DMS) onto an external system. As a result, we found it possible to progressively move toward complex solutions while navigating the solution space. Moreover, this guided design space exploration (DSE) also allows us to backtrack to former decisions later on. For example, backtracking would occur if we receive new priorities from decision makers, which would involve a re-evaluation of our constraints. Compared to existing solutions, we encompass the entire design process as to view the target system as a single project, rather than an aggregation of components. This, at last, helps with global optimization and the avoid-ance of side effects otherwise brought at the final integration phase. Though promising, we are still in the process of seeing if our method will remain simple enough against the combinatory explosion of an exhaustive architectural description.

This paper introduces a novel Sliding Mode-Based Observer tailored for a specific subset of nonlinear systems of order$\boldsymbol n$featuring Lipschitz nonlinearities. The study establishes stability conditions that ensure convergence of the estimation error (s) in finite time until order$\boldsymbol n$, thus, providing an accurate state (s) estimation without the necessity for disturbance matching conditions. Furthermore, the study presents an extension of the scope of application of the proposed method to tackle a unique scenario characterized by a time-varying and non-invertible function of the output dynamics of the system model. The effectiveness of the proposed observer is showcased through simulation examples.

This paper examines various non-linear state estimators for accuracy and robustness, estimating vehicle sideslip angle which is crucial for improving vehicle handling, stability, and safety in modern vehicle dynamic control systems. The study compares the performance of different state estimators under various driving conditions and driving scenarios, with a particular focus on a novel two-stage observer (of order n = 2) that combines a super-twisting sliding mode observer and a conventional sliding mode filter. The results demonstrate the effectiveness of the proposed observer, which outperforms other state estimators in terms of accuracy and robustness.

Automated and electrified ground vehicles depend on optimal chassis control to synergistically orchestrate different actuators. This integration aims to improve performance, comfort, handling, and stability. However, this optimal control problem necessitates several key parameters that are expensive or impractical to measure. Virtual sensing emerges as an efficient and cost-effective alternative, utilizing existing vehicle sensors to overcome these challenges. The present study proposes a Virtual Sensing Module (VSM) designed to cope with a chassis controller. It is capable of estimating tire forces, the vehicle’s SideSlip Angle (SSA), and the Tire-Road Friction Coefficient (TRFC). Furthermore, the VSM has the benefit of inferring the vehicle mass and adjusting the tire parameters. Importantly, the VSM operates effectively across coupled dynamics and under both standard and aggressive driving conditions. The proposed framework adopts a Data to Features to Decision structure: The Feature segment tracks the tire forces and SSA via Model-Based (MB) dynamic estimators. Fed by the outputs of the previous sub-module, the Decision block tackles the TRFC estimation challenge using a novel MB strategy. This strategy was benchmarked against various Data-Driven techniques for validation. The effectiveness of the VSM was verified through both simulation and real-world experimental data under varying road conditions. The findings demonstrate the module’s high accuracy, with minimal estimation errors, and its efficiency in both low and high excitation scenarios.

Advanced Driver Assistance Systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

Real-time knowledge of the vehicle mass is valuable for several applications, mainly: active safety systems design and energy consumption optimization. This work describes a novel strategy for mass estimation in static and dynamic conditions. First, when the vehicle is powered-up, an initial estimation is given by observing the variations of one suspension deflection sensor mounted on the rear. Then, the estimation is refined based on conditioned and filtered longitudinal and lateral motions. In this study, we suggest using these extracted events on two different algorithms, namely: the recursive least squares and the prior-recursive Bayesian inference. That is to express the results in a deterministic and statistical sense. Both simulations and experimental tests show that our approach encompasses the benefits of various works in the literature, preeminently, robustness to resistive loads, fast convergence, and minimal instrumentation.

Many Advanced Driver Assistance Systems (ADAS) and chassis systems can be found within the same passenger car. Most of these systems are controlled separately using standalone controllers. Several studies show the benefits of coordinating several systems especially in simultaneous operations. However, most of the studies tend to ignore the time-delays that exist in the control loop. This time-delays can destabilize the vehicle in severe situations. This paper provides a solution to face time-delays in the context of integrated ADAS and chassis systems. Results show the benefits of the control logic proposed in terms of stability and performance. This brings one step closer towards standardization of global vehicle motion control.

Most of automotive researches focus on autonomous vehicles. Studies regarding trajectory planning and trajectory tracking became preponderant. As in case of commercial ground vehicles there is a driver in the loop, one should raise the important question of how the trajectory should be tracked. In this paper, we investigate the influence of controlling integrated chassis systems on the vehicle's behavior. A fixed Model Predictive Control is used to track the trajectory. Tunable vehicle motion control is however used to provide different motion feelings. Results show that a specific trajectory could be followed in different manners. Therefore, vehicle dynamics can be and should be controlled in such a way to generate adaptive trust feelings to passengers in case of autonomous driving.

In this paper, we introduce a new formal modeling language that enriches the ground Abstract State Machine (ASM) formalism with the notion of time. Our temporal language is part of a larger HiTAsm language that also features hierarchical abstractions and is custom tailored for WCET analysis. The main differences with other ASM time languages is that HiTAsm correctness is proved using the same ASM metalanguage in which it is later integrated. The main purpose of this paper is to introduce the time in the HiTAsm and to prove its correctness.

In a typical Human-Machine Interaction (HMI) system, a task is performed by cooperation of the human and the automation component. The system adopts a cognitive architecture to model human psychology and makes optimum decisions on dynamic task allocation between human and the machine counterpart depending on the context. However, such architectures do not define how those systems are implemented in software. Various models involved in Model Driven Software Development (MDSD) approach in developing HMI systems is presented. This paper proposes a metamodel for modeling Non-Functional Properties (NFP) in HMI systems and provides a case study on assistive lane keeping in automobiles to demonstrate the approach.

In recent times, researchers in robotics have arrived at a consensus that there is no single efficient architecture or framework that satisfies all aspects of robotic system design. A majority of robotic architectures currently in use are developed in-house to meet the specific objectives of the research group. However, these frameworks have been designed in an ad-hoc manner and thus restricts reusability and scalability. In this paper, we propose an architecture description language `Architecture Modeling and Analysis Language (AMAL)' that homogenizes the framework development process in a formal way. AMAL is a radical approach that enables framework development based on custom requirements or by integrating existing heterogeneous architectural paradigms. The Open Semantics Framework in AMAL facilitates domain knowledge integration and promotes separation of concerns to build complex systems. We also position our approach in the SafeRobots framework, a model-driven software development tool-chain for robotics.

Recently, there is an encouraging trend in adopting model-driven engineering approaches for software development in robotics research. In this paper, currently available model-driven techniques in robotics are analyzed with respect to the domain-specific requirements. A conceptual overview of our software development approach called 'Self Adaptive Framework for Robotic Systems (SafeRobots)' is explained and we also try to position our approach within this model ecosystem.

A robotic system is a software intensive system that is composed of distributed, heterogeneous software components interacting in a highly dynamic, uncertain environment. However, no systematic software development process is followed in robotics research. In this paper, we present the core concepts that drive our framework `Self Adaptive Framework for Robotic Systems (SafeRobots)' for developing software for robotic systems. Two motivating examples are discussed: one discusses a system integration and knowledge representation problem, and the other explicates the issues associated with robotic system development in an industrial scenario. We also report on our progress on designing a meta-model based language - Solution Space Modeling Language, for problem-specific knowledge representation.

Article Free Access Share on Using abstract interpretation to define a strictness type inference system Author: B. Monsuez Laboratoire d'Informatique de l'École Normale Supérieure, CNRS URA 1327, 45 rue d'Ulm 75005 Paris Laboratoire d'Informatique de l'École Normale Supérieure, CNRS URA 1327, 45 rue d'Ulm 75005 ParisView Profile Authors Info & Claims PEPM '95: Proceedings of the 1995 ACM SIGPLAN symposium on Partial evaluation and semantics-based program manipulationJune 1995 Pages 122–133https://doi.org/10.1145/215465.215574Online:23 June 1995Publication History 4citation172DownloadsMetricsTotal Citations4Total Downloads172Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF