The interaction between heavy-duty vehicles turning right and non-motor vehicles going straight has led to severe traffic crashes. It is essential to evaluate the driving risk of heavy-duty vehicles in the right-turn phase. Increasingly, studies have explored some indicators associated with driving risk. Based on naturalistic driving data of 121 heavy-duty vehicles in Nanjing, this research combined factor analysis and K-means cluster algorithm to assess the driving risk of two scenarios, one without a blind spot warning and another with a blind spot warning during the right-turn phase. The results have concluded the driving characteristics of heavy-duty vehicles under different risk levels. It formed a set of driving risk level assessment methods for heavy-duty vehicles in the right-turn phase. This evaluation method is expected to identify high-risk right-turn behaviors of heavy-duty vehicles and provide some insights to practitioners for traffic management.

Zhang, Hedi, Fu, Yuanhang, Ma, Yongfeng, Chen, Shuyan

Chinese Ride-Hailing Drivers’ Adoption Intention of New Risk Visualization System: A Questionnaire Analysis Based on the Improved UTAUT2 Model

2025-01-7122

02/21/2025

Advanced Driver Assistance Systems (ADAS) have achieved significant progress worldwide, with the primary goals of enhancing driving safety, improving operational efficiency, and supporting vehicle automation. These systems are increasingly dependent on intelligent connected technologies, which enhance drivers' awareness and capacity to recognize and respond to potential road hazards in real-time. Within ADAS, risk visualization systems have become especially crucial, as they provide immediate alerts, thereby promoting safer driving behavior and enabling drivers to make more informed decisions on the road. This study expands upon existing frameworks by investigating the adoption of advanced risk visualization systems among Chinese ride-hailing drivers through an improved Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The improved model introduces two novel constructs: Technology Trust and Perceived Risk, addressing critical gaps in understanding safety-critical

Zhang, Jiayan, Li, Linheng, Pan, Yan, Qu, Xu, Ran, Bin

Cryptographic Method for Secure Object Segmentation for Autonomous Driving Perception Systems

12-08-01-0008

02/20/2025

The modern-day vehicle’s driverless or driver-assisted systems are developed by sensing the surroundings using a combination of camera, lidar, and other related sensors by forming an accurate perception of the driving environment. Machine learning algorithms help in forming perception and perform planning and control of the vehicle. The control of the vehicle which reflects safety depends on the accurate understanding of the surroundings by the trained machine learning models by subdividing a camera image fed into multiple segments or objects. The semantic segmentation system comes with the objective of assigning predefined class labels such as tree, road, and the like to each pixel of an image. Any security attacks on pixel classification nodes of the segmentation systems based on deep learning result in the failure of the driver assistance or autonomous vehicle safety functionalities due to a falsely formed perception. The security compromisations on the pixel classification head of

Prashanth, K.Y., Rohitha , U.M.

Leveraging Triggering Conditions for Efficient Scenario-Based Testing of Automated Vehicles

12-08-04-0035

02/19/2025

Scenario-based testing has become a central approach of safety verification and validation (V&V) of automated driving. The standard ISO 21448: Safety of the intended functionality (SOTIF) [1] proposes triggering conditions (e.g., an occluded traffic sign) as a new aspect to be considered to organize scenario-based testing. In this contribution, we discuss the requirements and the strategy of testing triggering conditions in an iterative, SOTIF-oriented V&V process. Accordingly, we illustrate a method for generating test scenarios for evaluating potential triggering conditions. We apply the proposed method in a two-fold case study: We demonstrate how to derive test scenarios and test these with a virtual automated driving system in simulation. We provide an analysis of the testing result to show how triggering condition-based testing facilitates spotting the weakness of the system. Besides, we exhibit the applicability of the method based on multiple triggering conditions and nominal

Zhu, Zhijing, Philipp, Robin, Howar, Falk

Model-Based Vehicle Mass Estimation for Enhanced Adaptive Cruise Control Performance

02-18-02-0009

02/19/2025

This study presents the development and integration of a vehicle mass estimator into the ZF’s Adaptive Cruise Control (ACC) system. The aim is to improve the accuracy of the ACC system’s torque control for achieving desired speed and acceleration. Accurate mass estimation is critical for optimal control performance, particularly in commercial vehicles with variable loads. The incorporation of such mass estimation algorithm into the ACC system leads to significant reductions in the error between requested and measured acceleration during both flat and uphill driving conditions, with or without a preceding vehicle. The article details the estimator’s development, integration, and validation through comprehensive experimental testing. An electric front-wheel drive van was used. The vehicle’s longitudinal dynamics were modeled using D’Alembert’s principle to develop the mass estimation algorithm. This algorithm updates the mass estimate based on specific conditions: zero brake torque, high

Marotta, Raffaele, D’Itri, Valerio, Irilli, Alessandro, Peccolo, Marco

TOC

99-07-01-0TOC

02/17/2025

TOC

Tobolski, Sue

Real-Time Driver Drowsiness Detection Using YOLOv8 with Whale Optimization Algorithm

09-13-01-0001

02/10/2025

Driver fatigue and drowsiness portray an integral role in the frequency of road accidents. Putting in place policies intended to alert drivers is imperative for averting accidents and saving lives. This work aims to improve road safety by devising a real-time driver drowsiness detection system. To accomplish this, drowsiness is detected using YOLOv8 algorithm optimized with the whale optimization algorithm (WOA). Key facial cues such as eye closure and yawning frequency are monitored to analyze driving behavior by the suggested approach. YOLOv8 model optimized with WOA processes video streams in real time and sets off an alarm on the graphical user interface (GUI) dashboard based on the output. The proposed approach was investigated using two datasets namely UTA-RLDD and D3S. A 640 × 640 pixel image with a frame rate of 50 fps was used in the investigation. The mAP at 0.5 (mean average precision at 0.5 IoU (intersection over union) threshold) of drowsiness detection system using UTA

Nandal, Priyanka, Pahal, Sudesh, Sharma, Tripti, Omesh, Omesh

Advanced Driver Assistance Systems for Driver Health and Fatigue Monitoring Using Camera, Biometric Sensors, Telematics and Machine Learning

2025-28-0195

02/07/2025

Automotive industries focus on driver safety leading to raising improvements and advancements in Advanced Driver Assistance Systems (ADAS) to avoid collisions and provide safety and comfort to the drivers. This paper proposes a novel approach toward Driver health and fatigue monitoring systems that uses cabin cameras and biometric sensors communicating continuously with vehicle telematics systems to enhance real-time monitoring and predictive intervention. The data from the camera and biometric sensors is sent to the machine learning algorithm (LSBoost) which processes the data and if anything is wrong concerning the driver's behavior then immediately it communicates with vehicle telematics and sends information to the emergency services. This approach enhances driver safety and reduces accidents caused due to health-related driver impairment. This system comprises several sensors and fusion algorithms are applied between different sensors like cabin camera and biometric sensors, all

Bhargav, Matavalam

Software Architecture for Autonomous Vehicles using MATLAB Simulink

2025-28-0190

02/07/2025

Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication

Ann Josy, Tessa, Sadique, Anwar, Thomas, Merlin, Manaf T M, Ashik, Vr, Sreeraj

Automotive Engineering: February 2025

25AUTP0202/06/2025

All the tech that's fit to print After CES revealed the next generation of ADAS and AV tech, as well as in-cabin communications, here's what the landscape looks like as we move into 2025. Suppliers prepare for a drop in EV market share Tier 1s that kept a foot in both the electric and ICE worlds say they're prepared for the changes ahead. A week with the Hyundai Way From 'Being a First Mover in Hydrogen' to cute city EVs, Hyundai's future technology plans run the gamut. Editorial Recuperating from AI-induced CES fatigue Supplier Eye Near-term focus can obscure reliable path forward Bosch goes right with wrong-way alerts, racing hand control Gentex improves dimmable glass visors with vanity mirror Ceres Holographics, Appotronics team for windshield-wide laser HUD Marelli says new BMS offers deeper insight into state of EV battery cells AVSC develops best practices for traceable AV safety inspection protocols 2025 BMW M5 first drive: Stuffed like schnitzel with PHEV tech Product Briefs

Neural Network-Enhanced Model Predictive Cruise Control for a Heavy Truck with LLM-Assisted Data Collection for Training

2025-01-7033

01/31/2025

Predictive Cruise Control (PCC) is a promising approach for improving fuel efficiency and reducing operational costs in heavy trucks. However, its implementation using conventional Nonlinear Model Predictive Control (NMPC) methods is hindered by computational limitations, often restricting the use of long-horizon slope information. This paper addresses these challenges by proposing a neural network-enhanced slope-adaptive NMPC framework. A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is employed to integrate long-horizon slope information and dynamically update control parameters, effectively overcoming computational constraints of traditional NMPC. To further enhance efficiency, an automated simulation scheduling system is developed, leveraging Large Language Models (LLMs) and expert knowledge to optimize parameter tuning and streamline data collection, significantly reducing training overhead. Validation on a high-fidelity simulation platform

Han, Xiao, Song, Kang, Lv, Qing Fang, Zhang, Yi, Xie, Hui

Enhanced Adaptive Cruise Control Strategies Considering Front and Rear Vehicles

10-09-01-0007

01/29/2025

Adaptive cruise control (ACC) systems have increasingly become more robust in adapting to the motion of the preceding vehicle and providing safety and comfort to the driver. But conventional ACC hangs with a concern for rear-end safety in the presence of traffic or aggressive car maneuvers. It often leads to getting dangerously close to the vehicle behind in scenarios where there is less space and time for the rear vehicle to adjust. This research article develops an ACC approach that considers the rear vehicle in addition to the front vehicle, thereby ensuring safety with the rear vehicle without compromising the safety of the front vehicle. Two novel methodologies are devised to enhance the ACC system. The first approach involves utilizing fuzzy logic to associate the inputs with the throttle and brake based on the inference rules within a fuzzy logic controller overseeing both vehicles. The other utilizes a cascaded model predictive control (MPC) system framework that integrates a

Sharma, Vishrut, Sengupta, Somnath, Ghosh, Susenjit

A Reinforcement Learning-Based Parameter Tuning Approach for a Secure Cooperative Adaptive Cruise Control System

12-08-04-0033

01/25/2025

Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the

Javidi-Niroumand, Farahnaz, Sargolzaei, Arman

Data-Driven Performance Evaluation of Machine Learning for Velocity Estimation Based on Scan Artifacts from LiDAR Sensors

12-08-04-0032

01/21/2025

Light detection and ranging (LiDAR) sensors are increasingly applied to automated driving vehicles. Microelectromechanical systems are an established technology for making LiDAR sensors cost-effective and mechanically robust for automotive applications. These sensors scan their environment using a pulsed laser to record a point cloud. The scanning process leads in the point cloud to a distortion of objects with a relative velocity to the sensor. The consecutive generation and processing of points offers the opportunity to enrich the measured object data from the LiDAR sensors with velocity information by extracting information with the help of machine learning, without the need for object tracking. Turning it into a so-called 4D-LiDAR. This allows object detection, object tracking, and sensor data fusion based on LiDAR sensor data to be optimized. Moreover, this affects all overlying levels of autonomous driving functions or advanced driver assistance systems. However, since such

Haas, Lukas, Haider, Arsalan, Kastner, Ludwig, Kuba, Matthias, Zeh, Thomas, Jakobi, Martin, Koch, Alexander Walter

SAE TOMORROW TODAY - Matching Buyers & Sellers to Advance SDV Development

1349701/16/2025

From evolving consumer expectations to changing business models to updates to the car itself, there is no doubt that software is radically transforming the automotive industry--and companies must adapt now or risk being left behind. The rise of smartphones has permanently altered consumer expectations, and software-defined vehicles (SDVs) are expected to provide experiences akin to those found on mobile devices. Preferences for infotainment and safety features through advanced driver-assistance systems (ADAS) are also increasingly central to purchase decisions. In an effort to meet these demands and accelerate the development of SDVs, SDVerse was developed as a B2B sales marketplace for buyers and sellers of automotive software. The matchmaking platform is available to all OEMs, suppliers, and other companies with relevant software offerings and tools. To learn more, we sat down with Prashant Gulati, Chief Executive Officer, to discuss the role of SDVerse in the automotive industry's

Hineman, Marcie

SAE TOMORROW TODAY: How AI Alignment Makes AVs Safer

1349501/10/2025

In order for AVs to perform safely and reliably, we need to teach them the language of human preference and expectations--and accelerating AI alignment can do just that. Enter Kognic, the industry-leading annotation platform for sensor-fusion datasets (e.g., camera, radar, and LIDAR data). By helping companies gather, organize, and refine massive datasets used for training AI models, Kognic is helping to ensure that AD/ADAS perform reliably and meet safety standards--all while minimizing costs and optimizing teams. To learn more, we sat down with Daniel Langkilde, Co-Founder and CEO, to discuss why the future of autonomous driving depends on effectively managing AI-driven datasets and how Kognic is leading dataset management for safety-critical AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in

Hineman, Marcie

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