Mauricio Marcano

This paper presents an openly documented real-vehicle evaluation of an adaptive shared control system that integrates a nonlinear Model Predictive Controller (NMPC), a Driver Monitoring System (DMS) and a haptic Human Machine Interface (HMI). While similar concepts have been explored by several industrial players, detailed performance data and transparent methodologies remain scarce in the public domain. This work aims to help fill that gap by providing insights into the behavior and integration of such systems under real driving conditions. The system establishes an active safety envelope for manual driving scenarios, employing a shared control strategy that activates during potentially hazardous situations caused by driver misbehavior or external road challenges. A key contribution of this work is the real-world validation of the approach in a vehicle with human drivers, demonstrating how shared control can be effectively deployed beyond simulation. The proposed framework integrates three elements: 1) an NMPC-based shared controller that shifts control authority across three different modes (Electronic Power Steering, Shared Control, and Fully Automated) by adjusting its weights; 2) a DMS that triggers mode switching; and 3) a haptic feedback interface that communicates system state, transitions, and driver intervention requests. The controller enforces a lane-keeping safety envelope and intervenes only when necessary. Results from real-vehicle experiments across two use cases show that the system enhances safety by mitigating lane-departure risks and improving driver attentiveness.

The work described in this paper provides valuable insight into the novel PDI solutions to be developed for CCAM support within the Horizon Europe-funded AUGMENTED CCAM project. To this aim, two different test sites are being prepared in Madrid, Spain, for the implementation and demonstration of four cutting-edge solutions: Equipped Vulnerable Road Users (VRU) protection; Traffic Management Optimization based on Probe Vehicle Data (PVD) from CCAM; Emergency Vehicle approaching; and Ad-hoc on-demand unmanned aerial vehicle (UAV) based VRU protection for closed environments. This document unveils how the proposed services underline the vast potential of CCAM when synergized with advanced Infrastructure support, which is not only limited to the extension of the Operational Design Domain of Connected and Automated Vehicles. This approach also demonstrates PDI’s capacity to significantly enhance road safety, traffic efficiency and sustainability. Additionally, the importance of the Digital Twin is highlighted as an indispensable element for advanced traffic management and comprehensive infrastructure monitoring.

This paper examines the potential integration of haptic feedback on steering wheels for automated driving applications, with a particular focus on transitions between automated and manual modes, takeover requests, and warnings. An iterative, three-phase methodology was employed: (1) The initial set of haptic notifications was designed based on input from the literature review, (2) These notifications were then tested in a driving simulator to identify the most effective options, and (3) The selected notifications were evaluated in a dynamic simulator under realistic conditions, including noise, vibration, and harshness (NVH). User studies were conducted at each phase to gather subjective metrics and validate the usability of the haptic feedback. The results demonstrate that specific haptic patterns enhance driver situational awareness and improve transitions between driving modes compared to conventional auditory signals, contributing to safer human-machine interaction in automated vehicles.

The increase in maritime traffic due to the globalization of trade has led to an exponential growth in logistics operations and port traffic management is becoming increasingly complicated. The need to improve the efficiency, safety, and sustainability of operations is leading to a strong demand for automation in port processes. A significant challenge is the optimization and automation of loading and unloading systems, given their complexity and repetitive nature. Advances in automated systems, through ongoing research and application in various transportation scenarios, are addressing these challenges. However, automating vehicles alone is insufficient; it is also important to have a connected, infrastructure-based collaborative framework to manage the complex logistics of port operations effectively and in a synchronized manner. In this sense, ESTIBA+ 2022, a Spanish-funded project, is addressing this challenge, aiming to develop strategic technologies for Smart-Ports. Its goal is to design and validate a scalable collaborative framework that meets the specifications and functions of port services using Industry 4.0 technologies and advanced wireless communications through Connected Intelligent Transportation Systems (C-ITS). This article presents the proposed architecture and its validation in the comprehensive use case of the project, focusing on communication from the supervision platform to Automated Guided Vehicles (AGVs) to ensure optimal traffic flow and management in ports. Specifically, the scenario involves three different forklifts equipped with an On-Board Unit (OBU) that interact with each other and the infrastructure via Roadside Units (RSU). The outcome of the project shows that the framework can meet the requirements for Smart-Port logistics, showing the feasibility of the implementation with a collaborative maneuver between three forklifts, a supervision station, and connected traffic lights.

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle’s lane. The study’s goal is to assess the algorithm’s performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver’s sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles.

Shared control has gained considerable attention in the automated vehicle field in recent years, both from a theoretical point of view and also with multiple applications. The development of shared control systems was discussed in a previous review, which presented a taxonomy focused on control algorithms. However, it is still necessary to understand how these systems should be assessed in terms of system performance, driver behavior, cooperation, and road safety. This paper aims to review and classify evaluation methods used in recent studies with real drivers. Results of the present review showed that shared control continues to be of interest to researchers of automated vehicles. The methodology for system evaluation has evolved, with more participants, better testing platforms, and a greater number of comparison baselines. To guide the path toward implementing shared control features in commercial vehicles, this review aims to help researchers to perform relevant evaluation studies in future developments.

The Shared Control (SC) cooperation scheme, where the driver and automated driving system control the vehicle together, has been gaining attention through the years as a promising option to improve road safety. As a result, advanced interaction methods can be investigated to enhance user experience, acceptance, and trust. Under this perspective, not only the development of algorithms and system applications are needed, but it is also essential to evaluate the system with real drivers, assess its impact on road safety, and understand how drivers accept and are willing to use this technology. In this sense, the contribution of this work is to conduct an experimental study to evaluate if a previously developed shared control system can improve overtaking performance on roads with oncoming traffic. The evaluation is performed in a Driver-in-the-Loop (DiL) simulator with 13 real drivers. The system based on SC is compared against a vehicle with conventional SAE-L2 functionalities. The evaluation includes both objective and subjective assessments. Results show that SC proved to be the best solution for assisting the driver during overtaking in terms of safety and acceptance. The SC’s longer and smoother control transitions provide benefits to cooperative driving. The System Usability Scale (SUS) and the System Acceptance Scale (SAS) questionnaire show that the SC system was perceived as better in terms of usability, usefulness, and satisfaction.

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.

Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques.