Browse Topic: Automated driving systems
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.
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.
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.
The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. This system is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer graphics (CG) to configure the system and improve the accuracy of the AI models with the aim of expanding its use to other factories. Within this digital twin environment, it is possible to examine previous tasks by reproducing the vehicles, processes, cameras, and vehicle movements present at a production site. Utilizing this digital twin enabled a significant reduction in the labor required to implement the system.
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.
Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated an additional 5 collisions and did not mitigate 1 collision. In these 16 conflicts entered, 93% of serious injury risk was reduced by the Waymo Driver, whereas 84% of serious injury risk was reduced by the NIEON model. Further, in a case-by-case evaluation, the Waymo Driver’s collision avoidance led to reduced serious injury risk when compared to the NIEON model in every simulated event. The results of this paper demonstrate that a reference model like NIEON can be used to benchmark ADS responder performance in response to high-risk initiating behaviors performed by the current driving population.
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