Browse Topic: Automated vehicles
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential relationships and influences during the design phase. The novelty of this framework comes from providing a holistic evaluation that captures testing of interactions between modules in addition to component-level testing, while other frameworks focus on testing individual component behaviors. It also assesses the equality of two interactions, meaning it ensures that two interactions behave the same way for consistent results. Moreover, the framework serves as a valuable tool for AV designers and safety regulators to aid in establishing robust design and assessment approaches. This work highlights the need for a common framework to thoroughly test AVs and gain a holistic understanding of system interactions. Finally, the framework aims to understand how to mitigate potential influences leading to AV malfunctions to advance the development and deployment of safe and reliable Automated Vehicles. The work focuses on level 1 and level 4 automated driving features to simplify the work, although it can be from level 1 to level 5. Although framework performance is inherently difficult to quantify, this framework’s performance can be reflected through its ability to accurately capture system interactions for improved AV design and support a broader usability among AV stakeholders. In the future, the framework can be expanded to include additional elements, such as infrastructure or other vehicles, to analyze information provided to AVs, allowing experts from various domains to collaborate, create similar models, integrate them when feasible, and model the interactions in real-time.
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
Precision control in Level 4 Automated Vehicles is essential for enhancing operational efficiency, accuracy, and safety. This work, conducted as part of ARPA-E’s NEXTCAR program, focuses on developing a robust hardware and software control solution to enable drive-by-wire functionality. A previous publication by the authors presented the hardware solutions for overtaking stock vehicle controls. This paper focuses on a model-based and data-driven control algorithm to enable drive-by-wire functionality for longitudinal and lateral motion control for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle. This vehicle was equipped with a set of sensors and an onboard processing unit to enable Level 4 automation. For lateral controls, an algorithm was developed to command steering torque to the electronic power steering module, ensuring the vehicle could attain the desired steering angle position at varying speeds. The system leveraged feedforward and feedback mechanisms. Feedback controller gains were identified through frequency response analysis of the steering torque assist electric motor and were further refined during track testing. To optimize the controller’s response time, a feedforward function was developed using a physics-aware model of the vehicle's steering system. The independent feature selection for the model was guided by using the physics of the system. For longitudinal control, the control inputs included the positions of the brake and accelerator pedals sent to the stock ECU, with the desired speed as the setpoint. The setup used a combination of feedforward and feedback control to achieve the target acceleration or deceleration. These algorithms underwent extensive dynamometer and track testing to perform various maneuvers in conjunction with the automated driving system.
Edge detection is fundamental for intelligent vehicle applications, directly supporting ADAS functions such as lane detection, obstacle recognition, and scene understanding. The conventional Canny edge detection method exhibits notable shortcomings, especially in color-image processing, adaptive threshold selection, and preserving edge integrity under noisy conditions. In this study, we present an enhanced Canny edge detection framework tailored for ADAS-oriented intelligent vehicle systems, incorporating a quaternion-based weighted averaging scheme for color preservation, adaptive thresholds derived from gradient-amplitude histograms, multiscale edge localization via scale multiplication, and a novel gravitational-field-intensity operator for improved gradient robustness. Moreover, we extend the method to vanishing-point estimation an essential ADAS capability by performing precise intersection calculations combined with clustering techniques such as DBSCAN and RANSAC. Experimental evaluations demonstrate that the proposed algorithm markedly outperforms traditional approaches in edge clarity, localization accuracy, and noise resilience, underscoring its promise for strengthening ADAS perception modules in intelligent vehicles.
This paper contains Part 2 of a two-part paper series proposing potential regulatory approaches for occupant safety in Automated / Autonomous Vehicles (AVs) with unique seating configurations (stagecoach and campfire seating). Part 2 focuses on interior safety sensing, associated messaging, and ride control approaches both prior to and during a ride. Assessments are also proposed after significant vehicle braking and crash events. The proposed conditions are to be assessed in a static vehicle environment with humans segmented by occupant size and an infant dummy. On the vehicle seat and on the vehicle floor occupant detection conditions are proposed along with restraint usage detection conditions for vehicle seat belt usage, Child Restraint Seat (CRS) usage, CRS seat belt usage, and Lower Anchors and Tethers for Children (LATCH) system usage. These conditions may be detected by sensors / computer algorithms and human monitoring and thus are technology agnostic. The topics of animal detection and cargo detection are also discussed. Part 1 of this paper series (SAE paper 2026-01-0578) proposed using interior safety sensing as an alternative / a replacement for the National Highway Traffic Safety Administration (NHTSA) Federal Motor Vehicle Safety Standard 208 (FMVSS-208) Occupant Crash Protection unbelted in-position occupant compliance conditions. This paper proposes conditions involving occupant and seat belt restraint usage detection. This evaluation approach strives to prevent unbelted occupants and is an improvement over restraint countermeasures for unbelted occupants. This paper also discusses and proposes visual and audible safety messaging for prior to the ride occupant education and for occupant and restraint usage detection outcomes. Vehicle level ride control actions are suggested such as preventing a ride when improperly restrained occupants are detected. These approaches can be used in industry-wide regulatory next step contemplation for unique interior seating arrangement AVs. When adopted, these approaches would likely reside in an expanded version of FMVSS-208.
With the rapid development of automated driving and the increasing adoption of “zero-gravity” seats, the crash safety of highly reclined occupants has become a critical issue. The current THOR dummy, designed for frontal impacts in the standard upright posture, exhibits limitations when directly applied to reclined seating configurations, including insufficient spinal flexion capability and excessive posterior pelvic rotation. In this study, the thoracolumbar spine kinematics of the THUMS human body model, reconstructed against post-mortem human subject (PMHS) tests, were analyzed. A two-segment linear fitting was employed to characterize a “dummy-like” spinal flexion response, yielding a virtual rotational hinge located near the thoracolumbar joint of the original THOR model. The characteristic rotation angle obtained from THUMS showed a strong linear correlation with the flexion moment of the T12–L1 vertebrae. Based on this relationship, the rotational joint of the THOR dummy was unlocked during impact and assigned a torsional stiffness of 600 Nm/rad. Additional modifications were implemented in the hip region to enhance model applicability. Comparative simulations demonstrated that the modified THOR model achieved closer agreement with PMHS responses than both the Hybrid III and the baseline open-source THOR models. In particular, the posterior pelvic tilt was reduced from approximately 20° in the baseline THOR to about 10° in the modified version. These results indicate that incorporating PMHS-based thoracolumbar flexion characteristics together with targeted hip modifications significantly improves the biofidelity of the THOR dummy for reclined-occupant crash scenarios, providing a solid foundation for future dummy development and safety assessment.
Lane centering is a critical active safety feature whose effectiveness depends on robust design and validation across diverse driving conditions. This paper presents the development of a Lane Centering Controller (LCC) using a structured model-based design workflow in MATLAB and Simulink. A kinematic bicycle model was employed to simulate vehicle dynamics and evaluate an angle based steering controller integrating both feedforward and feedback control paths. The controller was tested across multiple road geometries and speeds up to 65 mph to ensure tracking consistency and stability under nominal and perturbed conditions. Perception noise models for lane curvature and curvature rate were extracted from onboard camera data under controlled conditions, revealing Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw signal variability. The LCC maintained a peak lateral offset within ±0.35 m and lateral jerk within ±9 m/s3, while respecting a steering comfort limit of ±3 Nm, thereby satisfying both functional and driver comfort requirements. The MATLAB based workflow also facilitated requirement traceability and automated test case validation, enabling quantitative comparisons of control response across different speeds and curvature transitions. These results establish a clear link between simulation fidelity and control performance, providing a reference for calibration transfer in higher fidelity environments. The paper concludes with discussion on extending the algorithm to real time Hardware-in-the-Loop (HIL) and Vehicle-in-the-Loop (VIL) platforms, demonstrating scalability toward full vehicle implementation and providing a validated framework for future high speed lane centering development.
Some Automated / Autonomous Vehicles (AVs) have unique seating configurations (stagecoach and campfire seating) which present expanded occupant safety challenges. Significant portions of the National Highway Traffic Safety Administration (NHTSA) Federal Motor Vehicle Safety Standards (FMVSS) do not yet align with AVs containing unique seating. This paper series takes the NHTSA occupant safety standard approach for conventional forward-facing seat vehicles where many compliance evaluations are in the frequently occupied front row and expands it to stagecoach and campfire AVs where the rear seating row is anticipated to be frequently occupied. The approaches proposed are from a logic-based safety-focused analysis and in many cases previously published material. The goal of this paper series is to offer regulatory proposals that enable equivalent performance for these AVs to existing forward-facing seating vehicle occupant safety standards and meet Executive Order 13045 on child safety. Part 1 (this paper) focuses on occupant protection for the front and rear seating rows in stagecoach and campfire seating AVs for: front impacts (FMVSS-208), windshield mounting (FMVSS-212), windshield glazing (FMVSS-219), rear impacts (FMVSS-301/305), head restraints (FMVSS-202a), head impacts (FMVSS-201), side impacts (FMVSS-214), roof crush (FMVSS-216a), ejection mitigation (FMVSS-226), and door pinch (a potential FMVSS-118 addition). Some of the proposals address occupant performance for vehicles without traditional leg and restraint reaction surfaces. In addition, an interior safety sensing approach that assesses if occupants are properly restrained before a ride can begin is proposed as an alternative / a replacement for unbelted in-position occupant performance compliance evaluations. This document also provides regulatory condition thoughts for AVs without a usable driver seat location. Part 2 (SAE paper 2026-01-0576) discusses interior safety sensing and associated messaging. These approaches can be used in industry-wide regulatory next step contemplation and deliberation for unique interior seating arrangement AVs, including public discussions, safety research, approach proposal development, and rulemaking efforts.
CES provided Bosch with another high-profile chance - as it did with its Super Bowl ads in 2025 and in 2026 - to expand its reach with non-industry customers through a livestreamed press conference that touched on power tools and home appliances. Tanja Rueckert, a member of Bosch's board of management, said that Bosch's expertise “bridges a gap that many others struggle to cross: the divide between the physical and the digital.” This advantage, she said, turned the company into an AI leader, with over 2,000 AI patents and a plan to have invested over 2.5 billion euros in AI by the end of 2027. On the automotive front, Bosch's efforts to connect the digital and physical worlds can be seen in a meaningful update to its Vehicle Motion Management system. The system now has capabilities that will let it control a vehicle's movement in six degrees of movement, which should minimize motion sickness, especially in automated driving vehicles. Bosch's hardware-agnostic software solution manages braking, steering, powertrain, and suspension systems, tuning them to meet a desired driving mode or adapting to the driving situation.
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