In the automobile industry, ensuring the safety of automated vehicles equipped with the automated driving system (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms, and electrical/electronic (E/E) systems for situational awareness and response. ISO 21448 is the standard for Safety of the Intended Functionality (SOTIF) that aims to ensure that the ADS operate safely within their intended functionality. SOTIF focuses on preventing or mitigating potential hazards that may arise from the limitations or failures of the ADS, including hazards due to insufficiencies of specification, or performance insufficiencies, as well as foreseeable misuse of the intended functionality. However, the challenge lies in ensuring the safety of vehicles despite the limited availability of extensive and systematic

Patel, Milin, Jung, Rolf, Khatun, Marzana

Automated Vehicles and Infrastructure Enablers: Dedicated Lanes

EPR2025007

04/24/2025

Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.

Coyner, Kelley, Bittner, Jason

Cross-Scene Traffic Target Detection and Recognition Based on Improver YOLOv5s

12-09-01-0003

04/24/2025

Abstract Aiming at the problem of insufficient cross-scene detection performance of current traffic target detection and recognition algorithms in automatic driving, we proposed an improved cross-scene traffic target detection and recognition algorithm based on YOLOv5s. First, the loss function CIoU of insufficient penalty term in the YOLOv5s algorithm is adjusted to the more effective EIoU. Then, the context enhancement module (CAM) replaces the original SPPF module to improve feature detection and extraction. Finally, the global attention mechanism GCB is integrated with the traditional C3 module to become a new C3GCB module, which works cooperatively with the CAM module. The improved YOLOv5s algorithm was verified in KITTI, BDD100K, and self-built datasets. The results show that the average accuracy of mAP@0.5 is divided into 95.1%, 72.2%, and 97.5%, respectively, which are 0.6%, 2.1%, and 0.6% higher than that of YOLOv5s. Therefore, it shows that the improved algorithm has better

Ning, Qianjia, Zhang, Huanhuan, Cheng, Kehan

Learned Human Eye Gaze for Object Tracking: A Preliminary Study

09-13-01-0004

04/15/2025

Visual object tracking technology is the core foundation of intelligent driving, video surveillance, human–computer interaction, and the like. Inspired by the mechanism of human eye gaze, a new correlation filter (CF) tracking algorithm, named human eye gaze (HEG) tracking algorithm, was proposed in this study. The HEG tracking algorithm expanded the tracking detection idea from the traditional detection-tracking to detection-judging-tracking by adding a judging module to check the initial and retrack the unreliable tracking result. In addition, the detection module was further integrated into the edge contour feature on the basis of the HOG (histogram of oriented gradients) extracting feature and the color histogram to reduce the sensitivity of the algorithm to factors such as deformation and illumination changes. The comparison conducted on the OTB-2015 dataset showed that the overall overlap precision, distance precision, and center location error of the HEG tracking algorithm were

Jiang, Yejie, Jiang, Binhui, Chou, Clifford C.

Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles

J3216_202504 (Current)

04/14/2025

This document describes machine-to-machine (M2M)1 communication to enable cooperation between two or more traffic participants or CDA devices hosted or controlled by said traffic participants. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle equipped with an engaged driving automation system feature and a CDA device. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of conventional vehicles or pedestrians or cyclists carrying compatible personal devices), or compatible road operator devices (e.g., those used by personnel who maintain or operate traffic signals or work zones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the safer and more efficient movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be

Cooperative Driving Automation(CDA) Committee

Taxonomy and Definitions for Truck and Bus Automated Vehicle Systems

J3150_202504 (Current)

04/14/2025

This document provides definitions, terminology, and classifications for automated truck and bus vehicle applications. Vehicles covered by this document are those with a GVWR of more than 10000 pounds and where each vehicle utilizes driving automation systems that perform part or all of the driving task on a sustained basis and that range in level from some driving automation to full driving automation. The document also provides levels of driving automation that apply to the driving automation feature engaged in any given instance of operation of an equipped vehicle. A vehicle may be equipped with a driving automation system that is capable of delivering multiple driving automation features that perform at different levels; the level of driving automation exhibited in any given instance is determined by the feature(s) that are engaged. This document provides guidance for the elements of the dynamic driving task (DDT) for a truck or bus equipped with an Automated Driving System (ADS).

Truck and Bus Automation Safety Committee

Modular Dynamometer Testing Framework to Evaluate Energy Impacts of Longitudinal Automated Driving Systems

2025-01-8065

04/01/2025

As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impacts of automated longitudinal control on the overall energy consumption of the vehicle. The

Goberville, Nicholas, Hamilton, Kayla, Di Russo, Miriam, Jeong, Jongryeol, Das, Debashis, Ord, David, Misra, Priyashraba, Crain, Trevor

A Path Tracking Control Method Considering Vehicle Stability and Road Curvature

2025-01-8038

04/01/2025

The current research landscape in path tracking control predominantly focuses on enhancing tracking accuracy, often overlooking the critical aspect of passenger comfort. To address this gap, we propose a novel path tracking control method that integrates vehicle stability indicators and road curvature variations to elevate passenger comfort. The core contributions are threefold: firstly, we conduct comprehensive vehicle dynamics modeling and analysis to identify key parameters that significantly impact ride comfort. By integrating human comfort metrics with vehicle maneuverability indices, we determine the optimal range of dynamics parameters for maximizing passenger comfort during driving. Secondly, inspired by human driving behavior, we design a path tracking controller that incorporates an anti-saturation algorithm to stabilize tracking errors and a curvature optimization algorithm to mimic human driving patterns, thereby enhancing comfort. Lastly, comparative simulations with two

Lu, Jun, Zeng, Dequan, Hu, Yiming, Wang, Xiaoliang, Liu, Dengcheng, Jiang, Zhiqiang

Uncertainty Quantification in Machine Learning Using an Ensemble Approach with Gaussian Process Regression

2025-01-8199

04/01/2025

Machine learning has witnessed widespread adoption across various domains, bringing about transformative changes in decision-making, trend prediction, task automation, and personalized experiences. Despite the remarkable predictive capabilities of machine learning models, the associated uncertainty in their predictions remains a critical concern. Uncertainty estimation plays a pivotal role in ensuring robust decision-making, going beyond mere outcome prediction to quantify the model's confidence and potential error. This paper first presents a review of existing uncertainty quantification techniques in machine learning, including Monte Carlo dropout and ensemble methods, highlighting their advantages in addressing uncertainty as well as their limitations. Then, it presents an efficient and fast novel technique for uncertainty quantification using a combination of the ensemble technique and Gaussian process regression providing an accurate estimation of uncertainty bounds. Due to its

Chavare, Sudeep, Mourelatos, Zissimos P.

Deriving Architectural Requirements for SAE J3016 ^TM Level 3 System from Functional Safety Perspective

2025-01-8009

04/01/2025

Vehicles with SAE J3016TM Level 3 systems are exposed to road infrastructure, Vulnerable Road Users (VRUs), traffic and other actors on roadways. Hence safe deployment of Level 3 systems is of paramount importance. One aspect of safe deployment of SAE Level 3 systems is the application of functional safety (ISO 26262) to their design, development, integration, and testing. This ensures freedom from unreasonable risk, in the event of a system failure and sufficient provisions to maintain Dynamic Driving Task (DDT) and to initiate Minimum Risk Maneuver (MRM), in the presence of random hardware and systematic failures. This paper explores leveraging ISO 26262 standard to develop architectural requirements for enabling SAE Level 3 systems to maintain DDT and MRM during fault conditions and outlines the importance of fail-operability for Level 3 systems, from a functional safety perspective. At a high-level, UN Regulation No. 157 – Automated Lane Keeping Systems (ALKS) is used as a baseline

Mudunuri, Venkateswara Raju, Jayakumar, Namitha

Data Driven Quantitative Evaluation of Safety Controllability for Automated Vehicle

2025-01-8281

04/01/2025

With the development of automated vehicle (AV), it is essential to ensure their safety even in the presence of system faults or function inefficiency. Safety controllability refers to the ability to manage and control the vehicle, ensuring that it remains safe even in the presence of faults with unexpected conditions. This study proposed a data driven method to evaluate quantitatively safety controllability for AVs. Safety analysis is conducted to identify the potential hazard events. Taking system function and architecture into consideration, the failure modes of the vehicle hazards are identified with hazardous driving situation. Based on the identified failure modes, fault injection tests are conducted with critical scenarios. According to the vehicle dynamic performance, the improved analytic hierarchy process (AHP) can be explored to quantitatively evaluate the safety controllability based on fault injection test results. In particular, this study focuses on the case study to

Ye, Xiaoming, Yang, Yanding, Li, Lingyang, Zhang, Yaguo, Wang, Yongliang

A Motion-Aware Continuous Time LiDAR-Inertial SLAM Framework

2025-01-8044

04/01/2025

Towards the goal of real-time navigation of autonomous robots, the Iterative Closest Point (ICP) based LiDAR odometry methods are a favorable class of Simultaneous Localization and Mapping (SLAM) algorithms for their robustness under any light conditions. However, even with the recent methods, the traditional SLAM challenges persist, where odometry drifts under adversarial conditions such as featureless or dynamic environments, as well as high motion of the robots. In this paper, we present a motion-aware continuous-time LiDAR-inertial SLAM framework. We introduce an efficient EKF-ICP sensor fusion solution by loosely coupling poses from the continuous time ICP and IMU data, designed to improve convergence speed and robustness over existing methods while incorporating a sophisticated motion constraint to maintain accurate localization during rapid motion changes. Our framework is evaluated on the KITTI datasets and artificially motion-induced dataset sequences, demonstrating

Kokenoz, Cigdem, Shaik, Toukheer, Sharma, Abhishek, Pisu, Pierluigi, Li, Bing

Do Aftermarket and Original Equipment Forward Collision Warning Systems Respond the Same to Vehicles and Pedestrians?

2025-01-8677

04/01/2025

About 32% of registered vehicles in the U.S are equipped with automatic emergency braking or forward collision warning (FCW) systems [1]. Retrofitting vehicles with aftermarket devices can accelerate the adoption of FCW, but it is unclear if aftermarket systems perform similarly to original equipment manufacturer (OEM) systems. The performance of four low-cost, user-installable aftermarket windshield-mounted FCW systems was evaluated in various Insurance Institute for Highway Safety (IIHS) rear-end and pedestrian crash avoidance tests and compared with previously tested OEM systems. The presence and timing of FCWs were measured when vehicles approached a stationary passenger car at 20, 40, 50, 60, and 70 km/h, motorcycle and dry van trailer at 50, 60, and 70 km/h, adult pedestrian at 40 and 60 km/h, and child pedestrian crossing the road at 20 and 40 km/h. Equivalence testing was used to determine if FCW performance was similar for aftermarket and OEM systems. OEM systems provided a

Kidd, David, Floyd, Philip, Aylor, David

Efficient Sidewalk Route Planning on Aerial Images with Cost-Based A* and Mini-Max Objective Function

2025-01-8032

04/01/2025

With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for

Bao, Zhibin, Lang, Haoxiang, Lin, Xianke

Incorporating the Scenario-Based Framework Underpinned by Operational Design Domain (ODD) for Automated Driving Stack (ADS) Evaluation

2025-01-8067

04/01/2025

The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing

Chodowiec, Emil, Zhang, Xizhe, Mitchell, Joe, Baker, Peter, Khastgir, Siddartha, Jennings, Paul

Leveraging the Automated Mobility Partnership (AMP) to Support the Evaluation of Safety of the Intended Functionality (SOTIF) in Automated Driving Systems

2025-01-8674

04/01/2025

The Automated Mobility Partnership (AMP) is a consortium of industry and academic stakeholders dedicated to advancing Automated Driving Systems (ADS) through a comprehensive suite of tools, datasets, and methodologies. The AMP portal integrates events from over 35 million miles of naturalistic driving data including thousands of annotated crashes and near-crashes and a decade of U.S. police-reported crash data curated by the Virginia Tech Transportation Institute. The portal enables data discovery, visualization, processing, and analysis through secured web access. This paper briefly describes the AMP portal and examines its utility in developing and evaluating the safety of ADS using standardized processes. For the examination, we provide examples based on generic automated driving functions, guided by the Safety of the Intended Functionality (SOTIF) framework. The results show that AMP is instrumental in identifying recorded real-world cases in which the hazardous behavior of a

Antona-Makoshi, Jacobo, Williams, Vicki, Ali, Gibran, Sullivan, Kaye, Terranova, Paolo, Kefauver, Kevin, Hatchett, Alex

Hierarchical Feature-Based Localization Using Scene Graphs in Off-Road Navigation

2025-01-8046

04/01/2025

While numerous advancements have been made in autonomous navigation for structured indoor and outdoor environments, these solutions often do not generalize well to off-road settings. There are unique challenges in such settings such as unreliable GPS, limited computational and memory resources, and sparse environmental features, making navigation particularly difficult. In our work, we propose a novel data structure called Hierarchical Dynamic Scene Graphs (HDSG) to address these challenges. HDSG captures environmental information at different resolutions, integrating both geometric and semantic features. It enables various navigation tasks such as localization, loop closure, and human interaction through the visualization of environmental features for remote operators. We evaluated the performance of localizing a robot’s position within the world frame by comparing compact spatial descriptors extracted from semi-consecutive scene graphs, derived from 3D LiDAR point clouds. Compared to

Alam, Fardifa Fathmiul, Luricich, Federico, Li, Nianyi, Jia, Yunyi, Li, Bing

Path Tracking of Autonomous Vehicles with Model Predictive Control (MPC) Optimized with Particle Swarm Optimization (PSO)

2025-01-8036

04/01/2025

This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of

Eldesouky, Dina M.Mustafa, Abdelaziz, Taha Helmy, Mohamed, Amr.E

Avoiding the Crash: A Vision-Language Model Evaluation of Critical Traffic Scenarios

2025-01-8213

04/01/2025

Autonomous Vehicles (AVs) have transformed transportation by reducing human error and enhancing traffic efficiency, driven by deep neural network (DNN) models that power image classification and object detection. However, to maintain optimal performance, these models require periodic re-training; failure to do so can result in malfunctions that may lead to accidents. Recently, Vision-Language Models (VLMs), such as LLaVA-7B and MoE-LLaVA, have emerged as powerful alternatives, capable of correlating visual and textual data with a high degree of accuracy. These models’ robustness and ability to generalize across diverse environments make them especially suited to analyzing complex driving scenarios like crashes. To evaluate the decision-making capabilities of these models across common crash scenarios, a set of real-world crash incident videos was collected. By decomposing these videos into frame-by-frame images, we task the VLMs to determine the appropriate driving action at each frame

Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pesé, Mert D.

Dynamic Driving Task Assessment Scores for Scenarios Navigated by an OEM ADS-Equipped Vehicle

2025-01-8672

04/01/2025

One of the major issues facing the automated driving system (ADS)-equipped vehicle (AV) industry is how to evaluate the performance of an AV as it navigates a given scenario. The development and validation of a sound, consistent, and transparent dynamic driving task (DDT) assessment (DA) methodology is a key component of the safety case framework (SCF) of the Automated Vehicle – Test and Evaluation Process (AV-TEP) Mission, a collaboration between Science Foundation Arizona and Arizona State University. The DA methodology was presented in earlier work and includes the DA metrics from the recently published SAE J3237 Recommended Practice. This work extends and implements the methodology with an AV developed by OEM May Mobility in four diverse, real-world scenarios: (1) an oncoming vehicle entering the AV’s lane, (2) vulnerable road user (VRU) crossing in front of the AV’s path, (3) a vehicle executing a three-point turn encroaches into the AV’s path, and (4) the AV exhibiting aggressive

Wishart, Jeffrey, Rahimi, Shujauddin, Swaminathan, Sunder, Zhao, Junfeng, Frantz, Matt, Singh, Satvir, Como, Steven Gerard

Advancing Verification and Validation for ADAS and ADS

2025-01-8052

04/01/2025

Emerging automotive technologies like advanced driver assistance systems (ADAS) and automated driving systems (ADS) hold promise for improving safety for the traveling public; however, effective verification and validation (V&V) of these systems has proven to be challenging. Traditional testing methodologies may serve in limited cases for systems exhibiting low levels of automation, but recent studies show that these systems that have been brought to market perform poorly in practice. Further, these traditional methods do not serve for testing systems with high levels of automation where a human driver simply serves as a fallback ready user or is out of the loop altogether. New V&V methods are required to assess whether these systems can perform their intended functions in their intended operating environments, and to assess whether they can do so safely across the expansive and variable operating space. This paper presents an overview of ADAS and ADS challenges and novel approaches to

Thorn, Eric, Knisley, Veronica, Auchter, Joseph

Compensation Algorithm Development in Platform for Pedestrian ADAS Testing

2025-01-8066

04/01/2025

Test procedures such as EuroNCAP, NHTSA’s FMVSS 127, and UNECE 152 all require specific pedestrian to vehicle overlaps. These overlap variations allow the vehicle differing amounts of time to respond to the pedestrian’s presence. In this work, a compensation algorithm was developed to be used with the STRIDE robot for Pedestrian Automatic Emergency Braking tests. The compensation algorithm uses information about the robot and vehicle speeds and positions determine whether the robot needs to move faster or slower in order to properly overlap the vehicle. In addition to presenting the algorithm, tests were performed which demonstrate the function of the compensation algorithm. These tests include repeatability, overlap testing, vehicle speed variation, and abort logic tests. For these tests of the robot involving vehicle data, a method of replaying vehicle data via UDP was used to provide the same vehicle stimulus to the robot during every trial without a robotic driver in the vehicle.

Bartholomew, Meredith, Nguyen, An, Helber, Nicholas, Heydinger, Gary

Development of a Rule-Based Lidar Lane-Keeping Assistance System for Autonomous Vehicles Applications

2025-01-8025

04/01/2025

Lane-keeping is critical for SAE Level 3+ autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S.-approved level 3 vehicles are equipped with lidar, likely for accelerating active safety. Lidar offers direct distance measurements, allowing rule-based algorithms compared to camera-based methods, which rely on statistical methods for perception. Furthermore, lidar can support a more comprehensive and detailed approach to studying lane-keeping. This paper proposes a module perceiving oncoming vehicle behavior, as part of a larger behavior-tree structure for adaptive lane-keeping using data from a lidar sensor. The complete behavior tree would include road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the lane-keeping behavior, type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm was evaluated using

Soloiu, Valentin, Mehrzed, Shaen, Kroeger, Luke, Pierce, Kody, Sutton, Timothy, Lange, Robin

Controllability and Driving Automation

2025-01-8679

04/01/2025

A key challenge for manufacturers of automotive systems, hardware components and software products with no contribution to driving automation is the stringent requirements imposed on elements while being integrated into vehicles with driving automation. The result is increased development cost and low reusability. For such elements or components with no contribution to driving automation, their functions and failure modes remain unchanged when comparing vehicle integration with and without driving automation. The influence of driving automation is not accounted for in the current approach of classifying risk while conducting a Hazard Analysis and Risk Assessment (HARA). Functional safety standards for on-road vehicles rely on human intervention as a parameter to classify risk. Since current safety standards for on-road vehicles are not inclusive of driving automation concepts, classification of risk, based on existing definitions of parameters such as controllability, leads to

Shah, Mihir, Ibarra, Ireri

Developing a Safety Management System for the Automated Vehicle Industry

2025-01-8673

04/01/2025

Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV

Wichner, David, Wishart, Jeffrey, Sergent, Jason, Swaminathan, Sunder

Dynamics Analysis and Stability Control of a Lunar Unicycle Self-Balancing Robot

2025-01-8301

04/01/2025

The unicycle self-balancing mobility system offers superior maneuverability and flexibility due to its unique single-wheel grounding feature, which allows it to autonomously perform exploration and delivery tasks in narrow and rough terrains. In this paper, a unicycle self-balancing robot traveling on the lunar terrain is proposed for autonomous exploration on the lunar surface. First, a multi-body dynamics model of the robot is derived based on quasi-Hamilton equations. A three-dimensional terramechancis model is used to describe the interaction between the robot wheels and the lunar soil. To achieve stable control of the robot's attitude, series PID controllers are used for pitch and roll attitude self-balancing control as well as velocity control. The whole robot model and control strategy were built in MATLAB and the robot's traveling stability was analyzed on the lunar terrain.

Shi, Junwei, Zhang, Kaidi, Duan, Yupeng, Wu, Jinglai, Zhang, Yunqing

Safe Deployment of AI and ML Based Software and Algorithms in ADAS Systems

2025-01-8071

04/01/2025

As the autonomy of ADAS features are moving from SAE level 0 autonomy to SAE level 5 autonomy of operation, reliance on AI/ML based algorithms in ADAS critical functions like perception, fusion and path planning are increasing predominantly. AI/ML based algorithms offer exceptional performance of the ADAS features, at the same time these advanced algorithms also bring in safety challenges as well. This paper explores the functional safety aspects of AI/ML based systems in ADAS functions like perception, object fusion and path planning, by discussing the safety requirements development for AI/ML systems, dataset safety life cycle, verification and validation of AI systems, and safety analysis used for AI systems. Among all the safety aspects listed above, emphasis is put on dataset safety lifecycle as that is not only the most important element for training ML based algorithms for ADAS usage, but also the most cumbersome and expensive. The safety characteristics associated with dataset

Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua, Chandrasekaran, Mukund

The Rain Test Domain: An Outdoor Testbed for Automotive Sensor Evaluation in Adverse Weather Conditions

2025-01-8058

04/01/2025

The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain plant features a wetted area of 600 square meters and a sprinkled rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of Radar, Lidar, and Camera

Feichtinger, Christoph Simon

Improving Vehicle Localization Confidence under Different Road Geometries

2025-01-8043

04/01/2025

A significant challenge to the scalability of automated driving systems is the potential unavailability of GPS information for localization. To address this issue, a methodology using a static 2D map of road and lane geometry and vehicle on board sensors data is proposed to ensure reliable localization and navigation for automated vehicles in GPS-denied situations. In this study, a dead reckoning system based on vehicle kinematics is implemented by using onboard sensor data from the vehicle's Controller Area Network (CAN). However, the kinematic dead reckoning estimate has error accumulation, the drift in the dead reckoning position estimate is eliminated by using an arc-length based map matching approach. This innovative approach was tested and validated at various safety-critical intersection scenarios, including four-way intersection, roundabout, slip-lane intersection, and curved road. This approach ensures the continuous and reliable localization of automated vehicles, thereby

Javed, Nur Uddin, Singh, Yuvraj, Tan, Shengzhe, Ahmed, Qadeer

Reproduction of Real-World Scenarios in CARLA: An Extension of CARLA Functionality

2025-01-8059

04/01/2025

Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications

Ai, Yan, Adhikari, Bikram, Park, Chung-Kyu, Kan, Cing-Dao, Wijesekera, Duminda

A Model for the Design of Automated Vehicle Event Data Recorders

2025-01-8678

04/01/2025

When vehicle accidents occur, investigators rely on event data recorders for accident investigations. However current event data recorders do not support accident investigation involving automated or self-driving vehicles when there is state information that needs to be recorded, for example ADS modes, changes in the ODD that the vehicle operates under, and the various states of vehicle features such as intelligent cruise control, automated lane changes, autonomous emergency braking, and others. In this paper, we propose a model to design new types of event data recorders that supports accident investigations involving automated vehicles when there is state information to be recorded. The model is generic enough to be adapted to any automation level and any set of automated vehicle functional features. The model has been instantiated to a specific ADAS system.

Pimentel, Juan

Assessing the Energy Consumption Impact of Intelligent Driving Technologies on Electric Vehicles: A Comprehensive Review

2025-01-8598

04/01/2025

With the continuous advancement of artificial intelligence technology, the automation level of electric vehicles (EVs) is rapidly increasing. Despite the improvements in travel efficiency, safety, and convenience brought about by automation, cutting-edge intelligent technologies also pose the potential of increased energy consumption, such as the computational power required by advanced algorithms and the energy usage of high-precision equipment, leading to higher overall energy consumption for connected or autonomous electric vehicles (CAEVs). To assess the impact of intelligent technologies on AEVs, this study innovatively provides a comprehensive evaluation of the impact of intelligent technologies on CAEV energy consumption from both positive and negative perspectives. After reviewing 59 relevant studies, the findings highlight energy savings achieved through Vehicle-to-Infrastructure and Vehicle-to-Vehicle cooperation as positive effects, while increased energy consumption from

Liu, Tianyi, Qi, Hao, Ou, Shiqi (Shawn)

Containerization Enhanced Systems Integration for Robotics Code Development and deployment

2025-01-8345

04/01/2025

Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain

Varpe, Harshal Babsaheb, Coleman, John, Salvi, Ameya, Smereka, Jonathon, Brudnak, Mark, Gorsich, David, Krovi, Venkat N

Introducing the ML FMEA

2025-01-8078

04/01/2025

Several challenges remain in deploying Machine Learning (ML) into safety critical applications. We introduce a safe machine learning approach tailored for safety-critical industries including automotive, autonomous vehicles, defense and security, healthcare, pharmaceuticals, manufacturing and industrial robotics, warehouse distribution, and aerospace. Aiming to fill a perceived gap within Artificial Intelligence and ML standards, the described approach integrates ML best practices with the proven Process Failure Mode & Effects Analysis (PFMEA) approach to create a robust ML pipeline. The solution views ML development holistically as a value-add, feedback process rather than the resulting model itself. By applying PFMEA, the approach systematically identifies, prioritizes, and mitigates risks throughout the ML development pipeline. The paper outlines each step of a typical pipeline, highlighting potential failure points and tailoring known best practices to minimize identified risks. As

Schmitt, Paul, Seifert, Heinz Bodo, Bijelic, Mario, Pennar, Krzysztof, Lopez, Jerry, Heide, Felix

MPC-Based Trajectory Tracking Controller for Autonomous Vehicles

2025-01-8026

04/01/2025

Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral

Pan, Shicong, Lu, Jun, Yu, Yinquan, Zeng, Dequan, Yang, Jinwen, Hu, Yiming, Jiang, Zhiqiang, Liu, Dengcheng

Prognostic Techniques in Automated Driving System (ADS) Vehicle Safety

2025-01-8094

04/01/2025

The recent advancements in fields such as sensors, AI, and IoT are majorly impacting the automotive industry. Automated Driving Systems (ADS) are developing rapidly, meaning that SAE J3016 Level 3 and above vehicles are quickly becoming a reality. As a result, maintenance of such systems becomes essential to ensure their safe and efficient operation. Prognostic techniques in particular are crucial to monitor the state of health and predicting the end of life for components. Prognostics engineering is being applied in many industries and for conventional automotive applications, but ADS is new technology, and the prognostics for these systems are still being developed and adapted. In this paper, we first present a review of the most used prognostic techniques across different safety-critical domains such as aerospace, power, and manufacturing. Then, we summarize the main challenges that must be faced to successfully develop novel approaches for prognostics of ADS components and provide

Merola, Francesco, Hanif, Athar, Lami, Giuseppe, Ahmed, Qadeer, Monohon, Mark

Automated Analysis and Cleaning of Manufacturing Data: The Power of Automation Playbooks

2025-01-8332

04/01/2025

High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured

Jan, Jonathan, Preston, Joshua, Juncker, John

AUTONOMOUS TRUCKING TECH HITS HIGH GEAR

25TOFHP04_02

04/01/2025

While some developers of autonomous technology for commercial trucks have stalled out, there's renewed energy to deliver augmented ADAS and automated driving systems to mass production. After a tumultuous 2023 that saw several autonomous trucking startups pivot out of or exit the arena entirely, there has been a recent resurgence of investment and efforts to bring the vision of driverless freight fleets to reality. In the wake of firms like Embark, TuSimple and Waymo scaling back or rolling up operations, Aurora, Continental and Knorr-Bremse have all announced continued development of SAE Level 4 systems with the intention to deploy trucks using these systems at scale. OEMs such as Volvo Trucks have also announced updates to existing technologies that will augment current advanced driver-assistance systems (ADAS) to help human drivers become safer behind the wheel.

Wolfe, Matt

The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics

EPR2025003

03/31/2025

Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates who are calling for these systems to achieve higher levels of safety now. The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics explores these challenges that will fall on the National Highway Traffic Safety Administration (NHTSA) and automakers as they balance costs and benefits; establish reasonable regulations and standards; and determine how to improve, test, deliver, and use these systems successfully. Perhaps the most formidable challenge will

Chalmers, Seth

User Experience for Digitalized and Smart Cockpits and Cabins of Next-gen Mobility

EPR2025006

03/31/2025

This SAE Edge Research Report explores advancements in next-generation mobility, focusing on digitalized and smart cockpits and cabins. It offers literature review, examining current customer experiences with traditional vehicles and future mobility expectations. Key topics include integrating smart cockpit and cabin technologies, addressing challenges in customer and user experience (UX) in digital environments, and discussing strategies for transitioning from traditional vehicles to electric ones while educating customers. User Experience for Digitalized and Smart Cockpits and Cabins of Next-gen Mobility covers both on- and off-vehicle experiences, analyzing complexities in developing and deploying digital products and services with effective user interfaces. Emphasis is placed on meeting UX requirements, gaining user acceptance, and avoiding trust issues due to poor UX. Additionally, the report concludes with suggestions for improving UX in digital products and services for future

Abdul Hamid, Umar Zakir

Improving Safety of Autonomous Vehicles: A Verifiable Method for Graceful Degradation of Decision and Control Responsibilities

12-08-02-0021

03/24/2025

Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.

Aniculaesei, Adina, Aslam, Iqra, Zhang, Meng, Buragohain, Abhishek, Vorwald, Andreas, Rausch, Andreas

AVSC Best Practice for Automated Driving System-Dedicated Vehicle (ADS-DV) Immediate Post-Crash Behaviors and Interactions

AVSC-I-05-202503/04/2025

The behaviors and interactions of automated driving system-dedicated vehicles (ADS-DVs) following a crash can have continuing effects on road safety. As more ADS-DVs are commercially deployed and tested on public roads, it is important that systems are designed not only to prevent crashes, but to minimize the impact severity in situations where crashes cannot be avoided. Further injuries, vehicle and property damage, and secondary crashes can be mitigated based on appropriate ADS-DV behaviors and interactions following a crash. This best practice defines five general crash stages, focusing on the Immediate Post-Crash stage. It presents a framework with examples of ADS-DV behavior and interaction activities. It includes recommendations on decision making and evaluation of immediate post-crash ADS-DV behaviors. The document also highlights three dependencies that impact the behavior and interaction of an ADS-DV. The dependencies are paired with illustrative scenarios, followed by

Automated Vehicle Safety Consortium

Artificial Intelligence Use Cases for Ground Vehicle Applications

J3312_202502 (Current)

02/25/2025

This SAE Technical Information Report identifies use cases for AI technology applications to ground vehicles and transportation infrastructure. Whenever applicable, functional definitions and noted issues and concerns are provided in consistent with the current industry mobility practices and published peer-reviewed literature.

Artificial Intelligence

Benchmarking Large Language Models for Motorway Driving Scenario Understanding

2025-01-7146

02/21/2025

Systematic testing of Automated Driving Systems (ADS) requires finding relevant test cases. The extraction of critical cases, also called edge or corner cases, from naturalistic driving data is a complex task and often prone to multiple errors. Large Language Models (LLMs) have been employed for virtual testing of ADS in recent years; however, quantitatively benchmarking LLMs’ performance in this task has been barely investigated. In this paper, based on the characteristics of different LLMs, six LLMs were selected for benchmarking the LLMs’ ability to understand ADS functional scenarios on motorways. A novel scenario classification model was introduced to enhance the granularity of data categorization for motorway driving scenarios. Different driving scenarios, described in natural language, were defined for testing the capability of these LLMs to understand various scenarios and convert them into standardized structured data. To perform the benchmarking in a standardized manner, the

Zhou, Ji, Zhao, Yongqi, Yang, Aixi, Eichberger, Arno

Time-Varying Gain-Scheduled Path-Tracking Controller with Delay Compensation (TGDC) for Autonomous Vehicles

10-09-02-0012

02/19/2025

Path-tracking control occupies a critical role within autonomous driving systems, directly reflecting vehicle motion and impacting both safety and user experience. However, the ever-changing vehicle states, road conditions, and delay characteristics of control systems present new challenges to the path tracking of autonomous vehicles, thereby limiting further enhancements in performance. This article introduces a path-tracking controller, time-varying gain-scheduled path-tracking controller with delay compensation (TGDC), which utilizes a linear parameter-varying system and optimal control theory to account for time-varying vehicle states, road conditions, and steering control system delays. Subsequently, a polytopic-based path-tracking model is applied to design the control law, reducing the computational complexity of TGDC. To evaluate the effectiveness and real-time capability of TGDC, it was tested under a series of complex conditions using a hardware-in-the-loop platform. The

Hu, XuePeng, Zhang, Yu, Hu, Yuxuan, Wang, Zhenfeng, Qin, Yechen

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