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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Considerations for Vehicles With Automated Driving Systems: Seating Preference Study</title>
      <link>https://trid.trb.org/View/2761606</link>
      <description><![CDATA[Vehicles equipped with Automated Driving Systems (ADSs) are expected to perform part or all of the dynamic driving task. Vehicles equipped with an ADS that do not have manually operated driving controls are referred to as ADS-dedicated vehicles (ADS-DVs). At maturity, they enable novel vehicle designs, as there may be no further need for human drivers or driver-designated seating positions. This study investigated potential occupant behavior in rideshare ADS-DVs and developed a prototype human-machine interface (HMI) that considered relevant FMVSS requirements and the effective presentation of safety information to occupants. In this study, 42 participants were recruited under the guise of testing a rideshare application. Participants were accompanied by a confederate researcher who was there for safety purposes. These individuals rode in three study vehicles: a human-driven conventional vehicle, and two ADS-DV concept vehicles. The conventional vehicle and first ADS-DV were identical in layout, but the ADS-DV had no driving controls for a human driver. The second ADS-DV had an unconventional seating layout with one forward-facing and one rear-facing row of seats. In all vehicles, an HMI was developed that incorporated potential translations of relevant FMVSS from previous research. In 94 percent of the trips, participants were buckled in prior to requesting to start the ride. Those who received a seatbelt reminder (SBR) alert responded within approximately 1.5 seconds and were buckled in under 7 seconds. It was observed participants seemed to have preferred to sit in the front row of the conventional vehicle and the first ADS-DV concept. Sixty percent of participants rode facing forward while 40 percent faced rearward in the second ADS-DV concept that featured carriage-style seating. If they were to ride again, 79 percent indicated a preference for a conventional vehicle layout.]]></description>
      <pubDate>Mon, 24 Aug 2026 11:00:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761606</guid>
    </item>
    <item>
      <title>Towards AI-Driven Automated Driving Systems_Homologations Perspective</title>
      <link>https://trid.trb.org/View/2761783</link>
      <description><![CDATA[This paper examines the challenges and opportunities in homologating AI-driven Automated Driving Systems (ADS). As AI introduces dynamic learning and adaptability to vehicles, traditional static homologation frameworks are becoming inadequate. The study analyzes existing methodologies, such as the New Assessment/Test Methodology (NATM), and how various institutions address AI incorporation into ADS certification. Key challenges identified include managing continuous learning, addressing the "black-box" nature of AI models, and ensuring robust data management. The paper proposes a harmonized roadmap for AI in ADS homologation, integrating safety standards like ISO/TR 4804 and ISO 21448 with AI-specific considerations. It emphasizes the need for explainability, robustness, transparency, and enhanced data management in certification processes. The study concludes that a unified, global approach to AI homologation is crucial, balancing innovation with safety while addressing ethical considerations and public trust. Future research directions include developing real-time monitoring techniques and certification processes for adaptive systems.]]></description>
      <pubDate>Wed, 19 Aug 2026 13:48:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761783</guid>
    </item>
    <item>
      <title>Relevance and predictive role of human values in pedestrian and cyclist interactions with automated vehicles</title>
      <link>https://trid.trb.org/View/2735231</link>
      <description><![CDATA[As vehicle automation increases, vulnerable road users (VRUs) will need to negotiate right-of-way with level 4 and level 5 automated vehicles (AVs). Designing safe and comfortable interactions, therefore, requires a clear understanding of which human values are relevant for VRUs in such encounters. However, existing research lacks a comprehensive overview of relevant values, clear methods for quantifying them in concrete interaction scenarios, and evidence of their predictive role. To address these gaps, the present video-based laboratory study investigated VRU–vehicle interactions in a bottleneck scenario. Participants took the role of a pedestrian (nₚ = 60) or a cyclist (nc = 65; between-subjects factor) while interacting with an automated or a manual vehicle (within-subjects factor) and with or without another VRU (within-subjects factor). Value relevance was assessed quantitatively through ratings of value definitions and qualitatively by mapping values to participants' explanations of their behavioral intentions. The results show that a range of values, including comprehensibility, integrity, self-efficacy, and relaxedness, are relevant to VRU–AV interactions and should be considered in AV design. However, the predictive power of values for VRUs' behavioral intention was limited. Instead, participants' situation-independent decision preferences to wait or not wait were the strongest predictor of behavioral intention. These findings highlight the relevance of values for understanding VRU–AV interactions while indicating that their predictive power requires further investigation.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735231</guid>
    </item>
    <item>
      <title>Users’ Trust Evolvement in Fully Driverless Robotaxis During First Ride: An On-Road Study</title>
      <link>https://trid.trb.org/View/2698337</link>
      <description><![CDATA[Objective: This study investigates how users’ trust evolves during their first ride in a fully driverless robotaxi and how it can be affected by user characteristics, system design, and traffic scenarios. Background: As driving automation technology matures, driverless robotaxis have become available. Despite its immense economic and social potential, public acceptance can be strongly influenced by user trust. Previous research on trust in autonomous vehicles often relied on surveys, driving simulators, or “Wizard of Oz” methods, potentially introducing biases. Method: An on-road experiment was conducted in commercially operating fully driverless robotaxis on public urban roads. In total, 30 participants with no prior experience riding fully driverless robotaxis were recruited, comprising nondrivers (n = 10), and drivers with (n = 10) and without (n = 10) driving automation experience. Dynamic trust was collected at a 2-min interval during the ride, along with participants’ think-aloud for changes in trust. A cumulative link mixed model was used to assess the impact of past driving experience, demographics, and riding time on trust development. Results: Our findings revealed that dynamic trust increased gradually and stabilized over time, with user heterogeneity playing a moderating role in this process. Further think-aloud data analysis identified key factors in trust formation, including driving style, riding safety and comfort, and user interface design. Conclusion: Trust in driverless robotaxis builds progressively with real-world exposure, shaped by user characteristics, vehicle control, and interface design. Application: Our findings underscore the importance of considering user heterogeneity in fostering trust and acceptance of robotaxis.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698337</guid>
    </item>
    <item>
      <title>Mind the Gap: Quantifying Behavioral Fidelity in CARLA Using Naturalistic Drone Data</title>
      <link>https://trid.trb.org/View/2724751</link>
      <description><![CDATA[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.]]></description>
      <pubDate>Tue, 28 Jul 2026 12:27:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724751</guid>
    </item>
    <item>
      <title>Route-Based Sensor-Aware Speed Planning and Support for Safety Validation for Level-3 Automated Driving</title>
      <link>https://trid.trb.org/View/2724748</link>
      <description><![CDATA[Level-3 and higher automated driving systems require longitudinal speed strategies that remain consistent with both physical stopping feasibility and realistic sensing constraints. This paper presents a route-based, sensor-aware speed planning method that supports safety validation and explicitly couples longitudinal driving strategy with sensor field-of-view coverage. Based on a concrete route extracted from digital maps and enriched with fleet data, point-wise maximum speeds are computed considering road curvature, speed limits, and comfort constraints. From the resulting drivable speed profile, physically consistent stopping paths and their endpoints are calculated for each route position, accounting for friction limits, scenario-dependent deceleration capabilities, and system delays between perception and braking. The set of stopping paths is aggregated into a region of interest (ROI) representing the spatial area that must be reliably perceived to guarantee safe stopping. This ROI is overlaid with the geometric fields of view of camera, radar, and lidar sensors, enabling the definition of a compact and interpretable key performance indicator (KPI) based on the number of sensor modalities covering critical regions. Rather than evaluating a specific sensor configuration, the proposed KPI establishes a geometric interface between braking-based perception requirements and multi-modal sensing coverage. The approach reveals the structural sensitivity of perception demands to route geometry and braking assumptions and provides a systematic basis for perception-aware speed release decisions. The method is applicable to highways, interchanges, and other route types, and contributes a modular geometric framework for sensor-aware safety analysis in Level-3 and higher automated driving systems.]]></description>
      <pubDate>Tue, 21 Jul 2026 11:41:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724748</guid>
    </item>
    <item>
      <title>Automatic Synthetic Road Network Drivability
                    Evaluation</title>
      <link>https://trid.trb.org/View/2732197</link>
      <description><![CDATA[Simulation plays a significant role in the validation and verification of                     Automated Driving Systems (ADS). In a scenario-based validation strategy, the                     road and the actions of the traffic participants must be captured in a portable                     and flexible format for simulation. XML-based parametric models constitute a                     common combination upon which the static and dynamic aspects of the environment                     are captured. Although there are plenty of tools for generating these XML files                     there are few alternatives to verify their content. This paper suggests a method                     for converting and simplifying a synthetic road network into a graph for which                     the Chinese Postman Problem is solved. The resulting sequence can be converted                     back into a route that can be sampled to verify the drivability of the whole                     network. Once the network is verified, it can be safely used for simulation,                     increasing the speed at which ADS systems are developed. The graph                     representation can also be used to provide interactive feedback to LLMs (Large                     Language Model), which are increasingly used for automatic generation of roads                     and scenarios.]]></description>
      <pubDate>Tue, 21 Jul 2026 11:34:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732197</guid>
    </item>
    <item>
      <title>From risk to service: How a first Robotaxi ride shifts priorities, trust, and repeat use</title>
      <link>https://trid.trb.org/View/2713693</link>
      <description><![CDATA[Commercial robotaxi services are moving from pilots to routine operations, yet the link between a first ride and verified subsequent use is rarely documented: prior studies stop at post-ride attitudes or stated intention and do not observe whether riders return. This study is, to the authors’ knowledge, the first to trace this sequence in a commercial Level-4 service. In a three-wave field design, 64 adults in Shanghai, new to robotaxis, completed baseline measures, took a reimbursed ride, and completed a post-ride survey in which Best-Worst Scaling (BWS) captured the attribute-level reordering of their concerns; four weeks later, repeat use was verified from app booking records. BWS scores formed a service-versus-risk (S-R) index; trust was measured on an 11-item scale. After the ride, concerns shifted from low-frequency technical risks toward service attributes such as comfort, travel time, and reliability (mean S-R + 0.22; 81% shifting toward service). A more service-oriented profile predicted higher post-ride trust and stronger reuse intention, controlling for baseline levels. At four weeks, reuse intention predicted verified repeat use (odds ratio ≈ 2.25), while trust added no explanatory power once intention was included; the S-R profile showed a positive but inconclusive association. A first ride redirects attention from low-probability hazards to the service attributes riders use when deciding whether to book again. For operators, consistent service delivery, not safety messaging, is the early retention priority. Cybersecurity and data privacy, invisible during a short ride, still need their own communication channel.]]></description>
      <pubDate>Fri, 26 Jun 2026 13:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713693</guid>
    </item>
    <item>
      <title>Evaluation of Lane Changing by a Level 4 Autonomous Truck Based on the Reactions of Surrounding Vehicles</title>
      <link>https://trid.trb.org/View/2683016</link>
      <description><![CDATA[This study evaluated the lane-changing performance of a Level 4 (L4) autonomous truck using an international (UN-R79 ACSF Category C) lane-changing method. The evaluation was based on the thresholds calculated from the time to collision (TTC). The threshold calculated using the TTC, based on the driving response of the following vehicle, was clarified through driving simulator experiments as the time required for the L4 truck to overtake on expressways and change lanes when merging onto main roads. The thresholds are set for long and short blinker ON times, and in both cases, comply with UN-R79 ACSF Category C standard. When the blinker ON time was long, the risk of being overtaken by the following vehicle during the lane-change operation was observed. However, when the blinker ON duration was short, the number of instances in which the following vehicle applied brakes increased. Both situations are dangerous on expressways, and braking is higher than the frequency of overtaking. However, braking is not considered sudden braking based on the value of deceleration; therefore, the optimal threshold is to set the blinker on time to a lesser value. Even for lane changes at the scene of merging onto the main lane, thresholds were set for short and long blinker ON times, but both thresholds showed that merging was possible without significantly changing the driving behavior of the following vehicle. Furthermore, it was shown that the following vehicle takes action when the relative speed between the L4 truck and the following vehicle is around 14.0 m/s (50 km/h), the indicated threshold represents an acceptable driving behavior (avoidance behavior) for the following vehicle.]]></description>
      <pubDate>Thu, 25 Jun 2026 15:57:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683016</guid>
    </item>
    <item>
      <title>Human–automation interaction shapes safety performance across automation levels in safety–critical scenarios</title>
      <link>https://trid.trb.org/View/2712788</link>
      <description><![CDATA[Automated driving is widely expected to improve road safety, yet its realized safety performance in safety–critical scenarios remains uncertain because outcomes depend not only on system capability, but also on how drivers perceive risk, intervene, and interact with automation. This study establishes a unified driver-in-the-loop experimental framework for evaluating safety performance under human–automation interaction across multiple automation levels. Using high-fidelity driving simulator experiments covering representative safety–critical scenarios and SAE Levels 2–4, the authors collected a large-scale dataset of driver–automation interactions and safety outcomes. The results show that realized safety in safety–critical events is jointly shaped by automation capability and driver intervention behavior. As automation capability increased, driver intervention became less frequent and generally later, while overall safety performance improved. At the same time, a different cross-level regularity emerged among cases involving driver intervention: under the present experimental conditions, the collision risk conditional on intervention remained at a relatively stable non-zero level (approximately 26%), and both the intervention-onset risk state and the controllability boundary showed broadly similar patterns across levels. These findings indicate that automation level mainly changes whether and when drivers intervene, while the risk state at intervention onset and the overall effectiveness pattern of intervention remain broadly stable across levels. Model-based validation further showed that collision risk is primarily associated with the driver’s risk state at intervention onset and response latency. Overall, this study identifies a cross-level regularity in driver–automation interaction during safety–critical events and provides a driver-centered basis for understanding takeover limits and intervention-conditioned collision risk, with implications for the human-centered evaluation and design of safer automated driving systems.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712788</guid>
    </item>
    <item>
      <title>Safety-to-trust dynamics: Can automated vehicles navigate yellow-light dilemma zones from the driver’s perspective?</title>
      <link>https://trid.trb.org/View/2704168</link>
      <description><![CDATA[At signalized intersections, the yellow-light Dilemma Zone (DZ) is the roadway segment within which, at the onset of yellow, drivers cannot clearly determine whether stopping before the stop line or proceeding through the intersection is the safer maneuver, thereby elevating the risk of rear-end and right-angle crashes. Existing research primarily relies on signal-based or Connected Vehicle strategies (e.g., broadcasting signal phase/countdown information to drivers) to mitigate DZ-related safety risks, but these approaches often compromise intersection efficiency or impose additional cognitive demand on drivers. Recent advances in automated driving suggest a potential alternative, yet a clear gap remains: existing research has not conclusively quantified whether automated driving improves safety in DZ scenarios, particularly across Levels 3 and 4 automation, nor has it clarified whether such safety improvements transform into greater drivers’ trust during the transitional period in which human supervision and possible intervention are still required. To address this gap, this study develops a safety-to-trust framework to examine whether higher levels of automation improve objective safety in yellow-light DZ scenarios and whether these safety improvements constitute the mechanism through which automation shapes drivers’ trust. A driving simulator study was conducted by recruiting 52 participants to drive through a signalized intersection at the onset of yellow under Levels 0, 3, and 4, respectively. Driving behavior and its related safety performance were collected and analyzed. A linear mixed-effects model and a logistic regression model were applied to evaluate the effects of automation level on minimum time-to-collision (TTC) and traffic conflict, respectively, thereby assessing the safety benefits of different automation levels. Structural equation modeling (SEM) was further used to examine whether safety performance mediated the relationship between automation level and drivers’ trust. Results suggest that, under DZ conditions, higher automation levels significantly improve safety. SEM further reveals that increased automation improves safety, which in turn elevates drivers’ trust in automated driving, highlighting safety as the key mediating linkage between automation level and trust. Together, these findings quantify the safety benefits of automated driving systems in yellow-light DZ and clarify how those benefits shape trust, thereby providing an integrated basis for informing Automated Vehicle (AV) deployment and human–machine interface strategies at urban signalized intersections.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:56:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704168</guid>
    </item>
    <item>
      <title>The effects of camera perspective and augmentation on performance, situation awareness and mental workload of remote assistants of highly automated vehicles</title>
      <link>https://trid.trb.org/View/2700568</link>
      <description><![CDATA[In real-world operations, highly automated vehicles (HAVs, SAE Level 4) face many traffic situations they cannot cope with, e.g., situations with adverse weather. Remote human support may help to resolve such situations to increase robustness of HAV operations. In this task context, human-machine interfaces (HMIs) for remote operators of HAVs often present traffic situations similar to the driver's perspective. However, this first-person view is associated with shortcomings including the occlusion of relevant objects on the road or the distortion of distance and angle perception. These shortcomings may affect the performance of the remote operator. An experimental lab study with 37 participants was carried out to investigate if three different camera perspectives affect operator performance, situation awareness, and other operator-related variables in a remote assistance task at a busy urban intersection with mixed traffic. Additionally, the interplay of camera perspectives and video augmentation by visualizing additional sensor data was investigated in an environment with and without adverse weather due to fog. Results indicated that certain performance indicators including decision time were affected by camera perspective. The positive and compensatory impact of augmentation under poor visibility conditions in adverse weather was replicated. Findings suggest that the most suitable perspective highly depends on the specific scenario. The results will help design context-sensitive HMIs for remote assistance of HAVs.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700568</guid>
    </item>
    <item>
      <title>Developing geometric criteria to ensure Minimal Risk Conditions for highly automated vehicles</title>
      <link>https://trid.trb.org/View/2692654</link>
      <description><![CDATA[Connected and Automated Vehicles (CAVs) represent a transformative technology with the potential to significantly enhance road safety and improve mobility for all users. However, it is important to recognize that CAVs are not infallible; there will inevitably be situations in which these vehicles must come to a stop to ensure safety. This designated stopping location is called the Minimal Risk Condition (MRC). Achieving MRC should be a standard operational capability for SAE Level 4 + vehicles, which necessitates that the automated system performs a safe Dynamic Driving Task (DDT) fallback when required, especially in instances where the human driver may not be prepared to take control of the vehicle. This study proposes various solutions to facilitate MRC, including the use of hard shoulder, Emergency Refuge Lane (ERL), and Safe Harbor (SH). Initially, the authors examine the advantages and disadvantages of each solution, followed by an assessment of their respective capacities − experimental for ERLs and analytical for SHs. Based on these evaluations, some geometric design criteria and diverse solutions applicable to various highway types and interchanges are proposed. This work represents an important first step in addressing a critical topic that will receive further attention in the coming years, as the requirements for these zones are defined more precisely in relation to CAV penetration rates and Operational Design Domain (ODD) limitations. Consequently, it is essential that road and interchange design guidelines should be updated and adapted to incorporate these new facilities effectively.]]></description>
      <pubDate>Tue, 28 Apr 2026 17:05:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692654</guid>
    </item>
    <item>
      <title>Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions</title>
      <link>https://trid.trb.org/View/2692128</link>
      <description><![CDATA[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.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692128</guid>
    </item>
    <item>
      <title>Developing Safety Case for Automated Driving following UL 4600 &amp; ISO 5083</title>
      <link>https://trid.trb.org/View/2692097</link>
      <description><![CDATA[Automated Driving Systems (ADS) rely on AI algorithms, machine learning, and sensor fusion to perform autonomous driving tasks. Safety challenges arise due to the probabilistic behavior of AI/ML algorithms and the need to ensure safety within defined Operational Design Domains (ODDs). Traditional standards such as ISO 26262[3] (Functional Safety) and ISO 21448[4] (SOTIF) address hardware and software failures or functional deficiencies but are insufficient for higher-level autonomous systems (SAE Levels 3–5). To close this gap, additional standards such as UL 4600[1] and ISO 5083[2] provide complementary frameworks for ADS safety assurance. UL 4600[1] establishes a claim-based safety case encompassing the vehicle, infrastructure, and processes, emphasizing structured arguments supported by evidence and reasoning. It offers guidance on autonomy functions, V & V, tool qualification, dependability, and safety culture. ISO 5083[2] focuses on design, verification, and validation of ADS, extending safety lifecycles with system-level principles, risk criteria, and validation metrics. It defines the ADS safety case as proof of acceptable safety for specific features and environments, stressing safety-by-design, layered verification, and post-deployment monitoring, including cybersecurity. Together, UL 4600[1] and ISO 5083[2] enable a unified approach to safety assurance, aligning with Functional Safety and SOTIF principles. Their integration helps manufacturers evaluate ADS systematically, demonstrate risk acceptance, and maintain safety throughout the lifecycle.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692097</guid>
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