Browse Topic: Driving automation
In this paper, a distributed cooperative adaptive cruise control (CACC) strategy is designed for connected vehicle platoons under unknown control directions and time-varying speed constraints. To address vehicle control directions unknown problem arising from uncertain actuator and vehicle parameters, a Nussbaum-type gain is incorporated into the controller design processes, enabling stable operation without prior knowledge of the vehicle direction. Furthermore, the barrier Lyapunov function is employed to enforce speed constraints, ensuring all vehicles remain within safe and admissible velocity bounds. The designed leaderless scheme allows all vehicles to participate equally in coordination, enhancing robustness and scalability under a bidirectional communication topology. Finally, a simulation result is proposed to validate the effectiveness of the CACC strategy.
Autonomous driving systems are among the most complex software systems deployed in the commercial vehicle industry today. Modern autonomous driving platforms combine large-scale AI models, machine learning infrastructure, middle-ware, safety mechanisms and vehicle integration software into a tightly integrated software stack. This complexity introduces new cybersecurity challenges. Many of the threat modeling approaches used throughout the industry were developed around architectures with well-defined boundaries, limited software complexity and clearly documented interfaces. Autonomous driving systems operate differently.
In a world of SUVs, the S-Class sedan persists. The Mercedes luxury vehicle continues to be the vehicle of choice for wealthy drivers and passengers. The 2027 refresh continues that tradition with three powertrain options that support all S-Class occupants. Mercedes' halo vehicle received a myriad of updates for 2027, including a new grille, updated engines, and sensors. So many sensors. The result is an S-Class that doesn't stray far from traditional design, yet is built with an eye on the latest worldwide emission regulations and expanding in-car and driving technologies. That includes an SAE Level 4 S-Class in the near future, but let's focus on what I drove in Germany.
Despite a distinct lack of superheroes, the automated driving community scored big victories in San Diego at the end of July. The annual Automated Transportation Symposium opened just as the massive pop culture event that is ComicCon was leaving town. Iron Man and Aang stared out from nearby billboards as the conference tackled announcements regarding Zoox, changing automated vehicle regulations, and repeated discussions on how AVs need to improve interactions with first responders.
I'm not sure what to do with my hands. This is my typical response to any vehicle outfitted with a hands-free, eyes-on driver's assistance feature. After a few moments of the feature being enabled, I usually place my hands back on the steering wheel even though the vehicle is navigating the road ahead via an elaborate system of sensors and computers. The Lucid Gravity gets the same treatment. Still, it's handling the roadway deftly, without my input but definitely with my full attention. The second vehicle from automotive startup Lucid is a three-row SUV that more resembles a large station wagon or stylish minivan than the tall, upright sport utility vehicles that have overwhelmed the nation's roads. It's sleek, quick, modern, and filled with the latest technology. More importantly, it's enjoyable from behind the wheel with enough cargo space for even the most daring and bank-busting trip to Costco.
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
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.
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.
NHTSA is ready to make some deals regarding your safety. Peter Simshauser, the organization's chief counsel, said during SAE International's WCX 2026 this year that the agency is considering bringing back negotiated rule making, where a small number of automotive companies or other stakeholders can get together to make rules outside of the standard rulemaking procedures outlined in the Administrative Procedure Act (APA). “We feel confident that there is good space in NHTSA for Negotiated Rulemaking,” Simshauser said. “I can't commit to anything, particularly in front of this large audience, but we are anticipating activity in that regard. NHTSA has not traditionally done negotiated rule makings, but we are looking forward to it in a couple of spaces. It involves identifying a neutral party. Think of somebody like a law professor who's got subject matter expertise, bringing in people from across the stakeholder spectrum, both on the OEM side, on the supplier side, the safety advocate side, and trying to arrive at negotiated standards in a couple of areas that we've identified as ripe for this activity at this time.” Simshauser said tire and lighting standards have “been on the books for decades now and are long overdue for pretty deep overhauls.”
How CERTUS reshapes AV testing For automated driving system (ADS) engineers, verification and validation (V&V) are no longer a downstream activity and has increasingly become the program driver. Across the industry, validation costs and duration are now shaping product strategy. Independent analyses have suggested that up to a third of Level 4 vehicle development costs can sit within V&V. Physical mileage accumulation alone demands hundreds of millions, if not billions, of miles to achieve statistical confidence. That level of effort stretches timelines, absorbs engineering resources, and risks making higher levels of autonomy commercially unviable. The core problem is not a lack of testing. It is inefficiency in how testing is structured and how results are interpreted. Mileage accumulation remains necessary, but it is a blunt instrument. It repeatedly samples nominal conditions and under-represents rare, safety-critical edge cases. It generates volume, not necessarily safety insights. As the complexity of the driving scenarios that must be tested grows - expanding combinatorially as parameters are added for speed, numerous actors, realistic weather, road geometry, and behaviors - brute-force approaches don't scale.
It's good to be back on the pages of Automotive Engineering magazine after several years away. It's been more than a decade since former Uber CEO Travis Kalanick established what was then known as Advanced Technology Group (ATG) to develop its own automated driving system (ADS) and commented that robotaxis were an existential threat to his company. ATG's efforts didn't end well, with a fatal testing crash that killed a civilian. Eventually, Kalanick was pushed out of the company and ATG was sold to Aurora Innovation. Uber is no longer in the business of building an ADS, but it still has dreams of being the biggest operator of robotaxis in the world. Back in 2017, while ATG was in the midst of trying to develop an ADS, it struck a deal with Mercedes-Benz to deploy its future robotaxis on the Uber platform, much as human drivers do today. The goal was to fill the platform with as many driverless vehicles as possible from as many sources. Uber made it clear it was open to other partnerships. While Mercedes-Benz eventually cancelled the robotaxi that it was developing with Bosch, the idea stuck with Uber.
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