Browse Topic: Crash prevention

Items (120)

DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking

2025-01-0294

07/02/2025

We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in

Beutenmüller, Frank, Brostek, Lukas, Doberstein, Christian, Han, Longfei, Kefferpütz, Klaus, Obstbaum, Martin, Pawlowski, Antonia, Rössert, Christian, Sas-Brunschier, Lucas, Schön, Thilo, Sichermann, Jörg

Early Detection of the Loss of Control of Motorsport Vehicles

10-09-03-0029

06/30/2025

The article investigates how to detect as quickly as possible whether the driver will lose control of a vehicle, after a disturbance has occurred. Typical disturbances refer to wind gusts, obstacle avoidance, a sudden steer, traversing a pothole, a kick by another vehicle, and so on. The driver may be either human or non-human. Focus will be devoted to human drivers, but the extension to automated or autonomous cars is straightforward. Since the dynamic behavior of vehicle and driver is described by a saddle-type limit cycle, a proper theory is developed to use the limit cycle as a reference trajectory to forecast the loss of control. The Floquet theory has been used to compute a scalar index to forecast stable or unstable motion. The scalar index, named degree of stability (DoS), is computed very early, in the best case, in a few milliseconds after the disturbance has ended. Investigations have been performed at a dynamic driving simulator. A 14 DoF vehicle model, virtually driven by

Della Rossa, Fabio, Fontana, Matteo, Giacintucci, Samuele, Gobbi, Massimiliano, Mastinu, Giampiero, Previati, Giorgio

A Dynamic Hydroplaning Study: A Fluid–Structure Interaction Model for Road Vehicle Tire Analysis

15-18-03-0015

06/24/2025

Hydroplaning contributes to approximately 20% of traffic accidents during adverse weather conditions, with factors such as velocity, water film thickness, tire inflation, and vehicle weight playing significant roles. This study aims to simulate the hydroplaning phenomenon using a fluid–structure interaction model based on the coupled Eulerian–Lagrangian (CEL) capabilities of ABAQUS. Results reveal that vehicle linear velocity is a key determinant of hydroplaning risk, with a positive correlation observed. The findings suggest maintaining speeds under 50 km/h to mitigate hydroplaning risk, contingent on well-maintained, properly inflated tires. Multiple linear regression analysis further demonstrates correlations among velocity, tire inflation, quarter vehicle load, and water film thickness in predicting the reaction force between the tire and roadway. The proposed scheme provides a predictive mechanism for hydroplaning risk under varying conditions, offering valuable insights into

Aboelsaoud, Mostafa, Taha, Ahmed Abdelsalam, Abo Elazm, Mohamed, Elgamal, Hassan Anwar

Analysis and Evaluation of Roll Safety Thresholds of Tractor Semi-Trailer Vehicles during Turning Maneuvers Based on the Yaw Rate of Vehicle Bodies and Roll Safety Factor

02-18-04-0024

06/05/2025

This article aims to analyze and evaluate the roll safety thresholds (RSTs) and roll safety zones of tractor semi-trailer vehicles during turning maneuvers, using the roll safety factor (RSF) and yaw rate of the vehicle bodies. To achieve this, a full dynamics model is established using the multibody system method. This model is then used to survey and evaluate the vehicle’s motion state, using ramp steer maneuver (RSM) steering rules. In each survey case, the maximum values of RSF and yaw rate of vehicle bodies are synthesized in 3D data, with an initial velocity range of 40 km/h to 80 km/h and a magnitude of steering wheel angle range of 12.5° to 300°. These 3D data are used to determine the proposed values of RSF, which can be used as examples to set the threshold values of the yaw rate of vehicle bodies and roll safety zones. At a velocity of 60 km/h, the dynamic rollover threshold for proposed roll safety factor (RSFprop) is equal to 1, with corresponding values of 15.718°/s and

Hung, Ta Tuan

LightNetDehaze: Lightweight and Unsupervised Single-Image Dense Haze Removal

12-09-01-0005

05/26/2025

Image dehazing techniques can play a vital role in object detection, surveillance, and accident prevention, especially in scenarios where visibility is compromised because of light scattering by atmospheric particles. To obtain a high-quality image or as an initial step in processing, it’s crucial to restore the scene’s information from a single image, given that this is an ill-posed inverse problem. The present approach utilized an unsupervised learning approach to predict the transmission map from a hazy image and used YOLOv8n to detect the car from a clear recovered image. The dehazing model utilized a lightweight parallel channel architecture to extract features from the input image and estimate the transmission map. The clear image is recovered using an atmospheric scattering model and given to the YOLOv8n for car detection. By incorporating dark channel prior loss during training, the model eliminates the need for a paired dataset. The proposed dehazing model with fewer

Dave, Chintan, Patel, Hetal, Kumar, Ahlad

Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data

09-13-01-0006

04/12/2025

Hurricane evacuations generate high traffic demand with increased crash risk. To mitigate such risk, transportation agencies can adopt high-resolution vehicle data to predict real-time crash risks. Previous crash risk prediction models mainly used limited infrastructure sensor data without covering many road segments. In this article, we present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data that contain vehicle speed and acceleration information collected at a high frequency (mean = 14.32, standard deviation = 6.82 s). The dataset was extracted from a database of connected vehicle data for the evacuation period of Hurricane Ida on Interstate-10 in Louisiana. Five machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data. The results indicate that the Gaussian process boosting and extreme gradient

Syed, Zaheen E Muktadi, Hasan, Samiul

A Low-Entry Bus Body Structure Performance under the Conditions of the New Car Assessment Program Frontal Crash Tests

09-13-02-0007

04/09/2025

The New Car Assessment Program (e.g., US NCAP and EuroNCAP) frontal crash tests are an essential part of vehicle safety evaluations, which are mandatory for the certification of civil means of transport prior to normal road exploitation. The presented research is focused on the behavior of a tubular low-entry bus frame during a frontal impact test at speeds of 32 and 56 km/h, perpendicular to a rigid wall surface. The deformation zones in the bus front and roof parts were estimated using Ansys LS-DYNA and considered such factors as the additional mass (1630 kg) of electric batteries following the replacement of a diesel engine with an electric one. This caused stabilization of the electric bus body along the transverse axis, with deviations decreased by 19.9%. Speed drop from 56 to 32 km/h showed a reduction of the front window sill deformations from 172 to 132 mm, and provided a twofold margin (159.4 m/s2) according to the 30g ThAC criterion of R80. This leads to the conclusion about

Holenko, Kostyantyn, Dykha, Aleksandr, Koda, Eugeniusz, Kernytskyy, Ivan, Royko, Yuriy, Horbay, Orest, Berezovetska, Oksana, Rys, Vasyl, Humeniuk, Ruslan, Berezovetskyi, Serhii, Chalecki, Marek

Advanced Driver Assistance Systems (ADAS): Assessing the Efficacy of Non-Impact Testing for Evaluating the Performance of Frontal Collision Mitigation Technology

2025-01-8056

04/01/2025

As Automatic Emergency Braking (AEB) systems become standard equipment in more light duty vehicles, the ability to evaluate these systems efficiently is becoming critical to regulatory agencies and manufacturers. A key driver of the practicality of evaluating these systems’ performance is the potential collision between the subject vehicle and test target. AEB performance can depend on vehicle-to-vehicle closing speeds, crash scenarios, and nuanced differences between various situational and environmental factors. Consequently, high speed impacts that may occur while evaluating the performance of an AEB system, as a result of partial or incomplete mitigation by an AEB activation, can cause significant damage to both the test vehicle and equipment, which may be impractical. For tests in which impact with the test target is not acceptable, or as a means of increasing test count, an alternative test termination methodology may be used. One such method constitutes the application of a late

Kuykendal, Michelle, Easter, Casey, Koszegi, Giacomo, Alexander, Ross, Paradiso, Marc, Scally, Sean

Tube-MPC Based Path Tracking for I Vehicle Considering Data-Driven Stable Region and External Disturbances

2025-01-8262

04/01/2025

Path tracking is a key function of intelligent vehicles, which is the basis for the development and realization of advanced autonomous driving. However, the imprecision of the control model and external disturbances such as wind and sudden road conditions will affect the path tracking effect and even lead to accidents. This paper proposes an intelligent vehicle path tracking strategy based on Tube-MPC and data-driven stable region to enhance vehicle stability and path tracking performance in the presence of external interference. Using BP-NN combined with the state-of-the-art energy valley optimization algorithm, the five eigenvalues of the stable region of the vehicle β−β̇ phase plane are obtained, which are used as constraints for the Tube-MPC controller and converted into quadratic forms for easy calculation. In the calculation of Tube invariant sets, reachable sets are used instead of robust positive invariant sets to reduce the calculation. Simulation results demonstrates that the

Zhang, Haosen, Li, Yihang, Wu, Guangqiang

Daytime and Nighttime Performance Evaluations of the Automatic Emergency Braking and Forward Collision Warning Systems on a 2020 and 2022 Kia Telluride

2025-01-8699

04/01/2025

Testing was conducted in daytime and nighttime conditions to evaluate the performance of the Automatic Emergency Braking and Forward Collision Warning systems present on both a 2020 and 2022 Kia Telluride. The 2022 Kia Telluride was tested during the day at speeds between 35 and 70 miles per hour, while the 2020 Kia Telluride was tested both during the day and at night at speeds between 35 and 60 miles per hour (mph). The daytime testing of both the 2020 and 2022 Kia Telluride utilized a foam stationary vehicle target. The nighttime testing of the 2020 Kia Telluride utilized a live 2006 Chevrolet Tahoe as the target with the brake lights on. Testing measured the Time to Collision (TTC) values of the visual/audible component of the Forward Collision Warning (FCW) that was presented to the driver. Further, testing also quantified the timing and magnitude of the two-phase response of the Automatic Emergency Braking (AEB) system. The results of both sets of testing add higher speed FCW and

Harrington, Shawn, Patrick-Moline, Peyton, Nagarajan, Sundar Raman

Research on Dangerous Driving Behavior Recognition Algorithm Based on LSTM

2025-01-8204

04/01/2025

To address the issue of high accident rates in road traffic due to dangerous driving behaviors, this paper proposes a recognition algorithm for dangerous driving behaviors based on Long Short-Term Memory (LSTM) networks. Compared with traditional methods, this algorithm innovatively integrates high-frequency trajectory data, historical accident data, weather data, and features of the road network to accurately extract key temporal features that influence driving behavior. By modeling the behavioral data of high-accident-prone road sections, a comprehensive risk factor is consistent with historical accident-related driving conditions, and assess risks of current driving state. The study indicates that the model, in the conditions of movement track, weather, road network and conditions with other features, can accurately predict the consistent driving states in current and historical with accidents, to achieve an accuracy rate of 85% and F1 score of 0.82. It means the model can

Huang, Yinuo, Zhang, Miaomiao, Xue, Ming, Jin, Xin

Longitudinal Analysis of Forward Blind Zone Changes in Popular Vehicle Models (1997–2023)

09-13-01-0005

03/26/2025

Background. In 2022, vulnerable road user (VRU) deaths in the United States increased to their highest level in more than 40 years. At the same time, increasing vehicle size and taller front ends may contribute to larger forward blind zones, but little is known about the role that visual occlusion may play in this trend. Goal. Researchers measured the blind zones of six top-selling light-duty vehicle models (one pickup truck, three SUVs, and two passenger cars) across multiple redesign cycles (1997–2023) to determine whether the blind zones were getting larger. Method. To quantify the blind zones, the markerless method developed by the Insurance Institute for Highway Safety was used to calculate the occluded and visible areas at ground level in the forward 180° arc around the driver at ranges of 10 m and 20 m. Results. In the 10-m forward radius nearest the vehicle, outward visibility declined in all six vehicle models measured across time. The SUV models showed up to a 58% reduction

Epstein, Alexander K., Brodeur, Alyssa, Drake, Juwon, Englin, Eric, Fisher, Donald L., Zoepf, Stephen, Mueller, Becky C., Bragg, Haden

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

Advancing Road Sensing Technology: Functional Observer for Vehicle Road Profile Estimation

12-08-04-0038

03/24/2025

Having an in-depth comprehension of the variables that impact traffic is essential for guaranteeing the safety of all drivers and their automobiles. This means avoiding multiple types of accidents, particularly rollover accidents, that may have the capacity of causing terrible repercussions. The non-measured factors in the system state can be estimated employing a vehicle model incorporating an unknown input functional observer, this gives an accurate estimation of the unknown inputs such as the road profile. The goal of the proposed functional observer design constraints is to reduce the error of estimation converging to a value of zero, which results in an improved calculation of the observer parameters. This is accomplished by resolving linear matrix inequalities (LMIs) and employing Lyapunov–Krasovskii stability theory with convergence conditions. A simulator that enables a precise evaluation of environmental factors and fluctuating road conditions was additionally utilized. This

Saber, Mohamed, Ouahi, Mohamed, Naami, Ghali, El Akchioui, Nabil

Effects of Emergency Vehicle Warning Lighting System Characteristics on Driver Perception and Behavior

09-13-01-0003

02/28/2025

Secondary crashes, including struck-by incidents are a leading cause of line-of-duty deaths among emergency responders, such as firefighters, law enforcement officers, and emergency medical service providers. The introduction of light-emitting diode (LED) sources and advanced lighting control systems provides a wide range of options for emergency lighting configurations. This study investigated the impact of lighting color, intensity, modulation, and flash rate on driver behavior while traversing a traffic incident scene at night. The impact of retroreflective chevron markings in combination with lighting configurations, as well as the measurement of “moth-to-flame” effects of emergency lighting on drivers was also investigated. This human factors study recruited volunteers to drive a closed course traffic incident scene, at night under various experimental conditions. The simulated traffic incident was designed to replicate a fire apparatus in the center-block position. The incident

Bullough, John D., Parr, Scott, Hiebner, Emily, Sblendorio, Alec

Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

12-08-02-0020

02/24/2025

Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements

Fornaro, Gianfilippo, Törngren, Martin, Gaspar Sánchez, José Manuel

Quantifying Hydroplaning Risk: Insights from Water Film Thickness Measurements on Maryland Highways

02-18-02-0010

02/19/2025

Wet pavement conditions during rainfall present significant challenges to traffic safety by reducing tire–road friction and increasing the risk of hydroplaning. During high-intensity rain events, the roadway pavement tends to accumulate water, forming a film that can have serious implications for vehicle control. As the longitudinal speed of the vehicle increases, a water wedge forms in front of the tire, leading to partial loss of contact with the road. At critical hydroplaning speed, a complete water layer forms between the tire and the road. Although less common, dynamic hydroplaning poses severe risks when high-intensity rainfall coincides with high vehicle traveling speed, leading to a complete loss of control over vehicle steering capabilities. This study advances hydroplaning research by integrating real-world data from the Road Weather Information System (RWIS) with an existing hydroplaning model. This approach provides more accurate hydroplaning risk assessments, emphasizing

Vilsan, Alexandru, Sandu, Corina, Anghelache, Gabriel

Validating Human Factors-Based Classification Models for Safe and Efficient Mixed-Autonomy Interactions at Intersections Using a Virtual Simulator

12-08-02-0019

02/12/2025

The introduction of autonomous vehicles (AVs) promises significant improvements to road safety and traffic congestion. However, mixed-autonomy traffic remains a major challenge as AVs are ill-suited to cooperate with human drivers in complex scenarios like intersection navigation. Specifically, human drivers use social cooperation and cues to navigate intersections while AVs rely on conservative driving behaviors that can lead to rear-end collisions, frustration from other road users, and inefficient travel. Using a virtual driving simulator, this study investigates the use of a human factors-informed cooperation model to reduce AV reliance on conservative driving behaviors. Four intersection scenarios, each involving a left-turning AV and a human driver proceeding straight, were designed to obfuscate the right-of-way. The classification models were trained to predict the future priority-taking behavior of the human driver. Results indicate that AVs employing the human factors-informed

Ziraldo, Erika, Oliver, Michele

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

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

Letter from the Guest Editors

12-08-01-0001

12/10/2024

Letter from the Guest Editors

van Schijndel, Margriet, Sciarretta, Antonio, Op den Camp, Olaf, Krosse, Bastiaan

Design and Implementation of Advanced Safety Features in Two-Wheeled Vehicles for Accident Prevention and Anti-Theft System

2024-01-5229

12/10/2024

Embarking on exploring the cutting-edge domain of smart bike innovations, this study focuses primarily on enhancing safety and security measures. Through meticulous development and implementation, it introduces seven pioneering features to curb accidents and thwart theft incidents. These transformative functionalities encompass a spectrum of aspects, including cautionary systems for side stand and helmet usage, advanced alcohol detection mechanisms, and robust anti-theft measures employing ID card and password protocols. Moreover, integrating speed control mechanisms and automated brake activation on encountering speed breakers further elevates the safety quotient of the smart bike. By harnessing a diverse array of sensors such as RF, REED, ultrasonic, and gas sensors, these features collectively pave the way for a paradigm shift in road safety standards. The report meticulously details the intricacies of design, execution, and cost estimation, underscoring the transformative impact of

Mallieswaran, K., Agaramudhalvan, S., Nithya, R., Shuruti, R., Radhika, S.

Speed Control Assurance for Snowmobiles

J1222_202412 (Current)

12/05/2024

This SAE Standard provides minimum requirements and performance criteria for devices to prevent runaway snowmobiles due to malfunction of the speed control system.

Snowmobile Technical Committee

LDFA: Lightweight Dynamic Feature Aggregation for Multi-Modal Fusion

2024-01-7008

11/15/2024

Integrating 3D point cloud and image fusion into flying car detection systems is essential for enhancing both safety and operational efficiency. Accurate environmental mapping and obstacle detection enable flying cars to optimize flight paths, mitigate collision risks, and perform effectively in diverse and challenging conditions. The AutoAlignV2 paradigm recently introduced a learnable schema that unifies these data formats for 3D object detection. However, the computational expense of the dynamic attention alignment mechanism poses a significant challenge. To address this, we propose a Lightweight Cross-modal Feature Dynamic Aggregation Module, which utilizes a model-driven feature alignment strategy. This module dynamically realigns heterogeneous features and selectively emphasizes salient aspects within both point cloud and image datasets, enhancing the differentiation between objects and the background and improving detection accuracy. Additionally, we introduce the Lightweight

Feng, Xiaoyu, Zhang, Renhang, Chu, Zheng, Wei, Lina, Bian, Chen, Duan, Linshuai

Boosting Algorithms for the Accident Severity Classification

12-08-04-0030

10/17/2024

Background: Road accident severity estimation is a critical aspect of road safety analysis and traffic management. Accurate severity estimation contributes to the formulation of effective road safety policies. Knowledge of the potential consequences of certain behaviors or conditions can contribute to safer driving practices. Identifying patterns of high-severity accidents allows for targeted improvements in terms of overall road safety. Objective: This study focuses on analyzing road accidents by utilizing real data, i.e., US road accidents open database called “CRSS.” It employs advanced machine learning models such as boosting algorithms such as LGBM, XGBoost, and CatBoost to predict accident severity classification based on various parameters. The study also aims to contribute to road safety by providing predictive insights for stakeholders, functional safety engineering community, and policymakers using KABCO classification systems. The article includes sections covering

Babaev, Islam, Mozolin, Igor, Garikapati, Divya

Research on an Automatic Emergency Braking Control Method Based on Coordinated Wheel Deflection

09-12-03-0011

10/16/2024

Automatic emergency braking (AEB) systems play a crucial role in enhancing vehicular safety. Current research predominantly focuses on the longitudinal dynamics of vehicles, utilizing various control algorithms to improve braking effectiveness. However, there has been limited exploration into utilizing wheel deflection as a method to further enhance emergency braking performance. This study aims to contribute by proposing an advanced enhancement of the AEB system through coordinated wheel deflection strategies. In an emergency situation, when the speed of AEB-equipped vehicle drops to the set threshold due to wheel braking, the innovative control system will activate. The vehicle’s coaxial wheels will then execute a counter-deflection maneuver to maximize friction between the tires and the road surface. As a result, this approach reduces braking distance, thereby enhancing vehicle safety. The effectiveness of the proposed control algorithm is validated through combined simulation using

Lai, Fei, Xiao, Hao, Huang, Chaoqun

SAE TOMORROW TODAY: Improving Road Safety With ADAS and ADS

1347108/01/2024

Thousands of people die or are injured in automobile crashes each year. By leveraging in-vehicle advanced driver assistance systems (ADAS) and automated driving systems (ADS) more proactively, errors caused by human drivers can be reduced, resulting in less fatal and serious car crashes. To learn more about the future of road safety, we sat down with Seth Chalmers, Director of Traffic Engineering for Dibble, to discuss new NHTSA developments, the intersection of ADAS and autonomy, and his experience with Waymo in Phoenix.

Hineman, Marcie

Lighting and Visual Information for Vulnerable Road User (VRU) Safety: An Introductory Review

VRUSC-002-202405/03/2024

Injuries and fatalities among pedestrians, cyclists, scooterists, highway road workers, and safety and emergency personnel—often referred to as vulnerable road users (VRUs)—continue to rise at alarming rates worldwide. Emphasizing the urgent need for enhanced safety measures, this study, commissioned by the Vulnerable Road User Safety Consortium™ (VRUSC) and conducted by the Light and Health Research Center at the Icahn School of Medicine at Mount Sinai, evaluates the potential effectiveness of lighting and visual information systems in improving VRU safety. The white paper presents a synthesis of published research on lighting and markings from the perspective of both human drivers and machine vision systems. It identifies potential preliminary guidelines for the intensity, color, temporal, and spatial characteristics of lighting and visual information that can help prevent crashes involving VRUs. The study also highlights knowledge gaps that require further research, including the

Vulnerable Road Users Safety Consortium

Brake Pad Life Monitoring System Using Machine Learning

2024-26-0032

01/16/2024

In the context of vehicular safety and performance, brake pads represent a critical component, ensuring controlled driving and accident prevention. These pads consist of friction materials that naturally degrade with usage, potentially leading to safety issues like delayed braking response and NVH disturbances. Unfortunately, assessing brake pad wear remains challenging for vehicle owners, as these components are typically inaccessible from the outside. Moreover, Indian OEMs have not yet integrated brake pad life estimation features. This research introduces a hybrid machine learning approach for predicting brake pad remaining useful life, comprising three modules: a weight module, utilizing mathematical formulations based on longitudinal vehicle dynamics to estimate vehicle weight necessary for calculating braking kinetic energy dissipation; and temperature and wear modules, employing deep neural networks for predictive modeling. Notably, the model’s training leverages rig-level data

Iqbal, Shoaib, Bhambri, Mihir, lahase, Rahul

A Fast Search Method for Edge Hazardous Scenarios Based on Semi-Supervised Anomaly Detection

2023-01-7057

12/20/2023

Finding edge hazardous scenarios which appear very infrequently in the dataset than common hazardous scenarios is essential for implementing scenario-based testing of autonomous driving systems(ADs). However, it is difficult to evaluate the rarity of dynamic scenarios with huge scenario space high-dimensional time series, making it difficult to search for edge hazardous scenarios quickly. To solve this problem, this paper proposes a Semi-supervised anomaly detection method combining MiniRocket and DAGMM(Semi-MiniRocket-GMM, SRG), which treats edge hazardous scenarios as anomalous samples of common hazardous scenarios. SRG uses a small number of samples of common hazardous scenarios to guide interpretable feature extraction and clustering of a large amount of high-dimensional unlabeled temporal data and finds rarer edge hazardous scenarios based on anomaly evaluation to improve the coverage of test scenarios. The method is validated in the open-source natural driving dataset HighD

Li, Mengyu, Li, Fang, Guo, Zihan, Wang, Lifang

PROVIZIO eyes safety with radar, not lidar

23AUTP12_02

12/01/2023

Provizio promises its 5D Perception stack can safely compete with expensive lidar sensors at a fraction of the cost. “Safety first” is more than a catchphrase. For sensing company Provizio, it's the only way the transportation industry should introduce autonomous vehicles. In Provizio's view, using AV building blocks - technology such as automatic emergency braking and lane-keep assist - can be valuable in ADAS systems, but they should not be used to drive vehicles until the perception problem has been solved. “It's not that we're skeptical about autonomous driving, it's just that we strongly believe that the industry has taken this wrong path,” Dane Mitrev, machine learning engineer at Provizio, told SAE Media at September 2023's AutoSens Brussels conference. “The industry has looked at things the other way around. They tried to solve autonomy first, without looking at accident prevention and simpler ADAS systems. We are building a perception technology which will first eliminate road

Blanco, Sebastian

BRINGING ROBOTIC PLATFORMS FROM VEHICLE TESTING TO WARRIOR TRAINING

2024-01-4057

08/10/2023

ABSTRACT In order to expedite the development of robotic target carriers which can be used to enhance military training, the modification of technology developed for passenger vehicle Automated Driver Assist Systems (ADAS) can be performed. This field uses robotic platforms to carry targets into the path of a moving vehicle for testing ADAS systems. Platforms which are built on the basis of customization can be modified to be resistant to small arms fire while carrying a mixture of hostile and friendly pseudo-soldiers during area-clearing and coordinated attack simulations. By starting with the technology already developed to perform path following and target carrying operations, the military can further develop training programs and equipment with a small amount of time and investment. Citation: M. Bartholomew, D. Andreatta, P. Muthaiah, N. Helber, G. Heydinger, S. Zagorski, “Bringing Robotic Platforms from Vehicle Testing to Warrior Training,” In Proceedings of the Ground Vehicle

Bartholomew, Meredith, Andreatta, Dale, Muthaiah, Ponaravind, Helber, Nick, Heydinger, Gary, Zagorski, Scott

Adaptive Headlamp and Side Mirror Using Linear Regression Based on Raspberry Pi3

SAE-PP-0035007/31/2023

The greatest number of fatal traffic accidents occurs on curved roads at nighttime. In most cases, the late recognition of objects in the traffic zone plays an important role. This highlights to the importance of the role of automobile forward lighting systems. This paper developed a proto-type auto adjustable headlamp and mirror tilt to improve cost and reliability. Also, an adaptive mirror is implemented to remove the blind spots while taking turns. The methodology used here adaptive headlamps and mirrors are developed using Raspberry Pi3 as hardware and Python is used as programming language. Machine learning algorithm “Linear regression” is used for computing the output. Machine Learning Linear regression is considered here as it simple and efficient algorithm in terms of implementation and memory usage. Easily available components like Raspberry Pi3, LDR Sensor, ADXL Gyroscope are used and the design is developed to provide the steering mechanism for the headlamps and mirror which

Ekatpure, Rahul

When and How to Apply Automatic Emergency Brakes Based on Risk Perception and Professional Driver Emergency Braking Behavior

10-07-04-0028

07/26/2023

The key issues of automatic emergency braking (AEB) control algorithm are when and how to brake. This article proposes an AEB control algorithm that integrates risk perception (RP) and emergency braking characteristics of professional drivers for rear-end collision avoidance. Using the formulated RP by time to collision (TTC) and time headway (THW), the brake trigger time can be determined. Based on the professional driver fitting (PDF) characteristic, the brake pattern can be developed. Through MATLAB/Simulink simulation platform, the European New Car Assessment Programme (Euro-NCAP) test scenarios are used to verify the proposed control algorithm. The simulation results show that compared with the TTC control algorithm, PDF control algorithm, and the integrated PDF and TTC control algorithm, the proposed integrated PDF and RP control algorithm has the best performance, which can not only ensure safety and brake comfort, but also improve the road resource utilization rate.

Lai, Fei, Huang, Chaoqun, Jiang, Chengyue

A Multiple-Input Multiple-Output Radar-Based Rider Assistance System for Personal Light Electric Vehicles

2023-01-5040

07/06/2023

The use of personal light electric vehicles (PLEVs), such as electric scooters, has rapidly increased in recent years. However, their widespread use has raised concerns about rider safety due to their vulnerability in shared traffic spaces. To address this issue, this paper presents a radar-based rider assistance system aimed at enhancing the safety of PLEV riders. The system consists of an adaptive feedback system and a single-channel anti-lock braking system (ABS). The adaptive feedback system uses multiple-input multiple-output (MIMO) radar sensors to detect nearby objects and provide real-time warnings to the rider through haptic, visual, and acoustic signals. The system takes into account traffic density and uses online data to warn about obscured objects, thereby improving the rider’s situational awareness. Results from testing the feedback system show that it effectively detects potential collisions and provides warning signals, reducing the risk of accidents. The ABS is

Pyschny, Jan, Berger, Felix, Rothen, Samuel, Denker, Joachim, Frantzen, Michael, Roder, Felix, Kneiphof, Simon

Automatic Emergency Collision Avoidance of Straight-Crossing Intelligent Vehicle at a 90-Degree Intersection Based on Vehicle-to-Everything Technology

09-11-03-0010

04/27/2023

In order to reduce collision at a 90-degree intersection, an automatic emergency collision avoidance control method for intelligent vehicles based on vehicle-to-everything (V2X) technology is proposed. Most of the existing automatic emergency braking (AEB) control algorithms are designed for a single high-friction road with reference to the European New Car Assessment Programme (Euro NCAP) evaluation procedures, and they do not consider changes in road friction. Thus, it may be difficult to avoid collision successfully on a low-friction road. Although some studies have considered the variation of road friction, they are only applicable to straight-line rear-end collisions and cannot be directly applied to intersections. In addition, most studies regard the vehicle only as a particle, ignoring the actual dynamic characteristics of the vehicle. The main contribution of this article is to present an AEB control strategy by V2X technology, which can make the intelligent vehicle avoid

Lai, Fei, Yang, Hui, Huang, Chaoqun

Active Safety Roadside Concrete Divider Surrogate Recommendation

J3234/2_202303 (Current)03/08/2023

The scope of this document is to provide the design specifications/requirements/guidelines for concrete divider surrogates that represent actual concrete dividers to the in-vehicle sensors and can be used for performance assessment of such in-vehicle sensing systems in real-world test scenarios/conditions. Therefore, this document only includes the recommended concrete divider surrogate characteristics for automotive cameras, LiDARs, and/or radars. Concrete dividers are also known as concrete barriers [1].

Active Safety and Driver Support Systems Standards Committee

Volvo's electric high-wire act

23AUTP03_14

03/01/2023

Volvo calls its all-new EX90 SUV the safest and most technically adept model in the company's 95-year history, which includes such achievements as the world's first three-point automotive seat belt in 1959. Even before this luxury EV logs its first mile on global roads that take more than 1 million human lives every year, Volvo asserts the EX90 will eliminate up to one in five serious injury accidents, and one in 10 accidents overall. That claim is based not on fuzzy math, said Lotta Jakobsson, a 33-year company veteran and specialist in injury protection, but on Volvo's industry-unique accident database that's been a wellspring of company safety innovations since the 1970s.

Ulrich, Lawrence

Model-Based Systems Engineering of the Aft Collision Assist Advanced Driver Assistance System

15-16-03-0012

02/13/2023

The Aft Collision Assist (ACA) is an Advanced Driver Assistance System (ADAS) that is added to a vehicle and integrates with the native systems of that vehicle. The ACA is used to monitor and reengage a distracted driver of an approaching vehicle that the ACA system calculates will imminently rear-end the host vehicle. This work provides a brief overview of existing ADAS that perform similar functions, the regulatory statutes and requirements that impact the ACA functionality, and Model-Based System Engineering (MBSE) model diagrams of the ACA. The MBSE model diagrams presented are State Machine, Conceptual Data Model, Use Case, System Requirements, and Regulatory Requirements for the entire ACA system. The MBSE models and regulatory constraints presented within are used to refine and specify the ACA method of attracting a distracted driver’s attention.

Rictor, Andrew, Chandrasekar, Chandra V.

A Concise Camera-Radar Fusion Framework for Object Detection and Data Association

2022-01-7097

12/22/2022

Multi-sensor fusion strategies have gradually become a consensus in autonomous driving research. Among them, radar-camera fusion has attracted wide attention for its improvement on the dimension and accuracy of perception at a lower cost, however, the processing and association of radar and camera data has become an obstacle to related research. Our approach is to build a concise framework for camera and radar detection and data association: for visual object detection, the state-of-the-art YOLOv5 algorithm is further improved and works as the image detector, and before the fusion process, the raw radar reflection data is projected onto image plane and hierarchically clustered, then the projected radar echoes and image detection results are matched based on the Hungarian algorithm. Thus, the category of objects and their corresponding distance and speed information can be obtained, providing reliable input for subsequent object tracking task. Results shows that the fusion method

He, Yingjie, Zhao, Jian, Lyu, Nana, Li, Linhui, Liu, Pengbo

Optimization of Traffic Participant Detection Algorithm in Intelligent Roadside Scene

2022-01-7100

12/22/2022

In the intelligent transportation roadside object detection scene, due to the limited computing power of edge computing equipment, it is difficult to deploy large and complex object detection models, at the same time, most of detection models can not give consideration to precision and real-time. The complex road environment requires the model to have higher detection precision and faster detection speed. To solve this problem, an improved lightweight object detection model based on YOLOv4 is proposed(STDC_YOLO). The proposed model uses short-time dense network structure (STDC) to replace cross stage partial model structure (CSP), employs multi-scale feature fusion module which combines low-level feature and high-level feature to enrich feature information. In addition, the proposed STDC_YOLO employs re-parameterized VGG block (RepVGG) to improved parameter efficiency, thereby improving the inference speed. In order to improve the positioning precision of the predication bounding box

Yang, Zhe, Wang, Dengjiang, Li, Rui, Wang, Yajun, Ma, Gang, Ma, Bing

Perception-Aware Path Planning for Autonomous Vehicles in Uncertain Environment

2022-01-7077

12/22/2022

Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.

Chen, Zhan, Xiong, Lu, Tang, Chen

Identifying Typical Pre-crash Scenarios from CIDAS Data

2022-01-7117

12/16/2022

Identifying typical pre-crash scenarios can assist in determining potentially dangerous road traffic situations, and provide a basis for further expansions to vehicle safety test scenarios. Firstly, for the purpose of identifying the typical pre-crash scenarios of road traffic in China, 5983 accident cases in the China Traffic Accident In-depth Study (CIDAS) Database were screened. Next, the following variables have been identified as characteristic variables of scenario identification: personnel injury, the type of road, the form of accident, accident time, the cause of the accident (including human factors, vehicle factors, and environmental factors), and the casualties in accident. Then, the correlation analysis was conducted using the Pearson correlation coefficient for the selected variables. After that, the SSE (sum of squared errors) index was used to determine the number of cluster center. Finally, we described five typical Chinese road traffic pre-crash scenarios utilizing the

Lin, Miao, Li, Xiaohu, Wang, Peng, Zhu, Tong

The Effect of Driver’s Response Features on Safety Effectiveness of Autonomous Emergency Braking

2022-01-7131

12/16/2022

The Autonomous emergency braking system (AEB) has been widely equipped in the design and manufacture of vehicles as an active safety system for preventing rear-end collisions. It has shown great safety potential in preventing collisions and reducing collision injuries. However, there are differences in the response characteristics of drivers in emergency scenarios due to individual differences and driving habits. The impact of different driver types on the safety performance of AEB systems has not been evaluated. In this study, the typical driver response model was constructed by selecting driver response features representing alertness and braking. The AEB algorithm of distance and situation awareness was combined with the kinematic of vehicle before the collision to construct a simulation case based on the rear-end collisions in the China in-depth accident study database (CIDAS). The collision avoidance percentage, the impact speed, and the minimum relative distance were used as

Wei, Tianzheng, Kang, Kai, Zhu, Tong, Liu, Haoxue

Numerical Analysis of Wind-Break Fences for Truck Stability in Crosswind

02-16-02-0011

10/04/2022

Several accidents on the highways are due to strong crosswind conditions. The effectiveness of wind-break fences on a sudden strong crosswind has been investigated for a generic truck model. Two wind-break fences have been designed for stretching the rise time of aerodynamic loads. The dynamic response of the vehicle to crosswind while exiting a tunnel is simulated. Moving mesh CFD simulations and vehicle dynamics simulations are used to assess the effectiveness of the fences based on a safety index and the maximum lateral displacement of the vehicle. The proposed fences mitigate sudden aerodynamic loads and avoid the rollover of the vehicle.

Semeraro, Francesco Fabio, Cioffi, Antonio, Pellegrino, Emanuele, Schito, Paolo, Vignati, Michele

TELEOPERATED GROUND VEHICLE ROLLOVER PREVENTION VIA HAPTIC FEEDBACK OF THE ZERO-MOMENT POINT INDEX

2024-01-3994

08/10/2022

ABSTRACT Many rollover prevention algorithms rely on vehicle models which are difficult to develop and require extensive knowledge of the vehicle. The Zero-Moment Point (ZMP) combines a simple vehicle model with IMU-only sensor measurements. When used in conjunction with haptic feedback, ground vehicle rollover can be prevented. This paper investigates IMU grade requirements for an accurate rollover prediction. This paper also discusses a haptic feedback design that delivers operator alerts to prevent rollover. An experiment was conducted using a Gazebo simulation to assess the capabilities of the ZMP method to predict vehicle wheel lift-off and demonstrate the potential for haptic communication of the ZMP index to prevent rollover. Citation: K. Steadman, C. Stubbs, A. Baskaran, C. G. Rose, D. Bevly, “Teleoperated Ground Vehicle Rollover Prevention via Haptic Feedback of the Zero-Moment Point Index,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium

Steadman, Kathleen, Stubbs, Chandler, Baskaran, Avinash, Rose, Chad G., Bevly, David

Development of Automatic Door Lock System to Help Prevent Collisions between Opened Doors and Approaching Vehicles When Exiting Vehicle

2022-01-0068

03/29/2022

Collisions between opened doors and approaching vehicles such as bicycles are common occurrences in urban areas around the world. For example, in Chicago, 20% of all bicycle accidents involve collisions with doors, which occur over 300 times a year. In addition, there are concerns about a further rise in accidents due to the recent increase in home delivery services and bicycle commuting during the COVID-19 pandemic. Some advanced driver assistance systems (ADAS) that are designed to help prevent this type of accident have already been introduced. These systems detect approaching vehicles with sensors and alert the person opening the door via LED lights or a buzzer when the door is opened. The occupant must understand the meaning of the alert and stop opening the door quickly to prevent an accident. However, if the occupant is an elderly person or a child, it is difficult to stop opening the door quickly. One possible countermeasure for this issue is an ADAS that completely locks the

Takeuchi, Koji, Ishida, Masaho

Detection of Driver’s Cognitive States Based on LightGBM with Multi-Source Fused Data

2022-01-0066

03/29/2022

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction. Finally

Li, Jingyuan, Liu, Yahui, Ji, Xuewu, Tao, Shuxin

Vehicle Load Estimation Using Recursive Total Least Squares for Rollover Detection

2022-01-0914

03/29/2022

This paper will describe the development of a load estimation algorithm that is used to estimate the load parameters necessary to detect a vehicle’s proximity to rollover. When operating a vehicle near its handling limits or with large loads, vehicle rollover must be considered for safe operation. Vehicle mass and center of gravity (CG) height play a large role in a vehicle’s rollover propensity. Cargo and passenger vehicles operate under a range of load configurations; therefore, changes in load should be estimated. Researchers have often developed load estimation and rollover detection algorithms separately. This paper will develop a load estimation algorithm and use the load estimates and vehicle states to detect rollover. The load estimation algorithm uses total least squares and is broken into two parts. First, mass is estimated based on a “full-car” dynamic ride model. Next, the CG height and inertia are estimated using the previously estimated mass and a dynamic roll model

Hilyer, Trenton, Bevly, David M.

Physics-Based Misbehavior Detection System for V2X Communications

12-05-03-0020

03/04/2022

Vehicle to Everything (V2X) allows vehicles, pedestrians, and infrastructure to share information for the purpose of enhancing road safety, improving traffic conditions, and lowering transporation costs. Although V2X messages are authenticated, their content is not validated. Sensor errors or adversarial attacks can cause messages to be perturbed increasing the likelihood of traffic jams, compromising the decision process of other vehicles, and provoking fatal crashes. In this article, we introduce V2X Core Anomaly Detection System (VCADS), a system based on the theory presented in [1] and built for the fields provided in the periodic messages shared across vehicles (i.e., Basic Safety Messages, BSMs). VCADS uses physics-based models to constrain the values in each field and detect anomalies by finding the numerical difference between a field and and its derivation using orthogonal values. VCADS evaluation is performed with four real V2X field testing datasets and a suite of attack

Andrade Salazar, Alejandro Antonio, McDaniel, Patrick Drew, Sheatsley, Ryan, Petit, Jonathan

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