Driven System Research Articles

Smart Driving Technology for Non-Invasive Detection of Age-Related Cognitive Decline

Alzheimer’s disease (AD) and Alzheimer’s Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver’s biometrics, offering insights into a driver’s cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies.

SpotAttack: Covering Spots on Surface to Attack LiDAR-Based Autonomous Driving Systems

SpotAttack: Covering Spots on Surface to Attack LiDAR-Based Autonomous Driving Systems

A Maneuver Coordination Analysis Using Artery V2X Simulation Framework

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS).

Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems

Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems

Topological Floquet engineering of a three-band optical lattice with dual-mode resonant driving

We present a Floquet framework for controlling topological features of a one-dimensional optical lattice system with dual-mode resonant driving, in which both the amplitude and phase of the lattice potential are modulated simultaneously. We investigate a three-band model consisting of the three lowest orbitals and elucidate the formation of a crosslinked two-leg ladder through an indirect interband coupling via an off-resonant band. We numerically demonstrate the emergence of topologically nontrivial bands within the driven system, and a topological charge pumping phenomenon with cyclic parameter changes in the dual-mode resonant driving. Finally, we show that the band topology in the driven three-band system is protected by parity-time reversal symmetry. Published by the American Physical Society 2024

MonoFG: Monocular 3D Object Detection with Knowledge Distillation for Human-Centric Autonomous Driving Systems

Monocular 3D object detection is essential for identifying objects in road images, thus offering valuable environmental perception data that are crucial for human-centric autonomous driving systems. However, due to the inherent limitations of camera imaging, obtaining precise depth information from images alone is challenging, which hampers the accuracy of within-scene object localization. In this paper, we introduce a monocular 3D object detection method called MonoFG that uses knowledge distillation with separated foreground and background components to improve the accuracy of object localization. First, detached foreground and background distillation processes can strategically leverage the distinct positional information acquired from each location to optimize the produced global distillation effects. This step serves as the foundation for the subsequent feature and response distillation process, which focuses on the distilled foreground and background rather than isolated object distillation. Second, triple attention mechanism-based feature distillation intensifies the feature imitation and feature representation capabilities of the student network. Spatial and channel attention mechanisms encourage the student network to capture crucial pixels and channels from the teacher network, whereas a self-attention mechanism globally transfers the learned relationships between pixels. Third, localization error-based response distillation facilitates a clearer transfer of positional information from the teacher network to the student network. Only when the positioning ability of the teacher network exceeds that of the student network can knowledge be comprehensively distilled across both the foreground and background. Therefore, the distillation process is constrained to specific content, which is delineated by positioning errors that serve as the boundaries. Finally, experiments conducted on the KITTI benchmark dataset demonstrate that our method outperforms many well-known baseline methods in several representative evaluation tasks (e.g., 3D object detection and bird's-eye view (BEV) detection) involving human-centric autonomous driving systems.

Research on the Behavior Influence Mechanism of Users’ Continuous Usage of Autonomous Driving Systems Based on the Extended Technology Acceptance Model and External Factors

Research on the Behavior Influence Mechanism of Users’ Continuous Usage of Autonomous Driving Systems Based on the Extended Technology Acceptance Model and External Factors

Magnetic actuator driver system for laser scanning capsule endoscopy

This paper focuses on designing and implementing a power and area-efficient magnetic actuator driver interface integrated circuit for laser scanning capsule endoscopy. The proposed system contains a 3D-printed focus-adjusting actuator embarking a lens, multiple magnets, an external coil, battery, laser, and actuator driver integrated circuit with off-chip components. The actuator features multiple pantograph springs connected to the lens, as well as multiple magnets, enabling precise focusing capability through electromagnetic actuation. A magnetic actuator driver integrated circuit implemented in a commercial 180 nm CMOS process drives the coil at 32 Hz, which is the mechanical resonance frequency of the actuator. A novel control methodology for the driver has been devised, aimed at enhancing driving efficiency and mitigating total harmonic distortion. Simulations and measurements substantiate that the actuator can induce a 3.22 mm focal point displacement while the driver circuit delivers 9.62 mA (RMS) current to the 7.7 mH coil. Under these conditions, the system exhibits an aggregate power consumption of 11.48 mW, thereby achieving a power efficiency of 85.5%.

Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems

Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records

Superior safety is the main banner value of promoting autonomous vehicle (AV) technology, but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks would be overshadowed by technological limitations, regardless of their ability to mitigate or eliminate human error. This study aims to identify the key factors affecting crash severity by analyzing real-world AV crash data from the U.S. between 2015 and 2022. We integrated two open data sources from the California DMV and NHTSA. Mixed multinomial logit models incorporating the interaction effects were estimated using the crash severity level, addressing the observed and unobserved heterogeneities. Our results show that Advanced Driver Assistance System (ADAS) engagement is associated with a higher likelihood of slight injury crashes, whereas Automated Driving System (ADS) engagements show the opposite effect. In addition, we found that various environmental and road factors, including lighting conditions, weather, road type, and road surface conditions, significantly affect the severity of AV crashes. For instance, daylight conditions contribute to a lower likelihood of slight-injury crashes. On the other hand, driving under unfavorable weather conditions (cloudy, foggy, rainy, or snowy), on the highway, and on non-dry road surfaces are associated with an increase in the likelihood of severe injury crashes. Furthermore, several significant interaction effects were revealed. First, the mitigating effect of ADS engagement on the likelihood of slight injury crashes is reduced by the rear-end collision type. Second, the likelihood of slight injury crashes increases when AV interacts with heavy trucks on highways. Third, the likelihood of severe injuries increases when AVs collide with Vulnerable Road Users (VRUs) on urban streets. Overall, this research is expected to provide policymakers and AV manufacturers with valuable insights into AV safety, stressing that addressing the identified factors will lead to improved AV design and control algorithms.

Enhancing the Heat Dissipation Efficiency of Computing Units Within Autonomous Driving Systems and Electric Vehicles

With the rapid development of autonomous driving technology and electric vehicles, the demand for computing units in vehicles has surged, particularly for high-density computing related to artificial intelligence, sensor fusion, and real-time data processing. This increase has posed significant heat dissipation challenges for automotive electronic systems. Effective heat dissipation is essential for ensuring system stability, safety, and extending the lifespan of these systems. This study explores the thermal design of computing units in current autonomous driving systems and electric vehicles. The electronic components within the computing units generate significant heat, but due to environmental constraints, fans can not be used for internal cooling, as their operation can draw in dust from the environment, contaminating the internal electronic components. Therefore, computing units are often designed with a sealed structure, utilizing the thermal conductivity of metal casings to quickly dissipate internal heat sources. The results of this study show that comparing embedded cooling modules within the casing to commonly used thermal pads reveals that changing the materials of the cooling module can significantly enhance cooling performance, noticeably reducing the temperature of the internal electronic components and minimizing the risk of overheating. Additionally, this can improve the overall reliability and performance of the system, providing new ideas and application prospects for future automotive thermal design.

A Case Study on the Integration of Powerline Communications and Visible Light Communications from a Power Electronics Perspective.

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure.

Autonomous Multitask Driving Systems Using Improved You Only Look Once Based on Panoptic Driving Perception

Autonomous Multitask Driving Systems Using Improved You Only Look Once Based on Panoptic Driving Perception

Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous Driving Systems

Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous Driving Systems

Deep Reinforcement Learning for Autonomous Driving Systems

Autonomous driving systems (ADS) are poised to revolutionize the future of transportation, promising increased safety, efficiency, and convenience. Deep Reinforcement Learning (DRL) has emerged as a powerful approach to solving complex decision-making tasks in dynamic environments, making it a promising candidate for the development of intelligent autonomous vehicles. This paper explores the application of DRL techniques in autonomous driving, focusing on the integration of perception, planning, and control. We review state-of-the-art DRL algorithms, including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC), and examine their roles in enabling end-to-end learning for driving policies. Furthermore, we discuss the challenges inherent in deploying DRL in real-world autonomous driving scenarios, including sample inefficiency, safety constraints, and the sim-to-real gap. Finally, the paper presents case studies and experimental results that highlight the potential of DRL to improve autonomous vehicle performance in complex environments, while identifying future research directions to address open problems in the field.

Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review

The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things (IoT) technologies have driven the innovation of advanced autonomous driving systems (ADSs). Image classification deep learning (DL) algorithms immensely contribute to the decision-making process in ADSs, showcasing their capabilities in handling complex real-world driving scenarios, surpassing human driving intelligence. However, these algorithms are vulnerable to adversarial attacks, which aim to fool them in real-time decision-making and compromise the reliability of the autonomous driving functions. This systematic review offers a comprehensive overview of the most recent literature on adversarial attacks and countermeasures on image classification DL models in ADSs. The review highlights the current challenges in applying successful countermeasures to mitigating these vulnerabilities. We also introduce taxonomies for categorizing adversarial attacks and countermeasures and provide recommendations and guidelines to help researchers design and evaluate countermeasures. We suggest interesting future research directions to improve the robustness of image classification DL models against adversarial attacks in autonomous driving scenarios.

To Err is Automation: Can Trust be Repaired by the Automated Driving System After its Failure?

To Err is Automation: Can Trust be Repaired by the Automated Driving System After its Failure?

TRAINING OF DRIVERS WITH DISABILITIES: MODERN CHALLENGES AND DEVELOPMENT PROSPECTS

All citizens have equal constitutional rights and freedoms and are equal before the law (Article 24 of the Con-stitution of Ukraine). In this context, a number of legislative and normative legal acts have been adopted in Ukraine today, which establish guarantees of compliance with the rights, freedoms and legitimate interests of persons with disabili-ties, as well as determine special mechanisms for ensuring their social protection. An analysis of the state of the system of training drivers of vehicles from among persons with disabilities was carried out. Currently, there are only seven specialized educational institutions for training drivers with disabilities in Ukraine. The current situation regarding the number of specialized educational institutions for training drivers with disabilities, as well as the situation regarding the training of drivers with disabilities of category B only, do not meet the needs and do not meet the requirements of the law. In addition, typical programs for the training of all drivers of vehicles provide basic training, but do not take into account the specifics of training people with disabilities and their needs as future drivers of vehicles. Prospects for the development of the system of training drivers of vehicles from among persons with disabilities have been determined, which consist in the expansion of the network of specialized educational institutions. In this context, it should be noted that educational institutions for training drivers with disabilities should have, in particular: provision of training for all categories of vehicles; educational programs adapted to the needs of persons with disabilities; training cars with manual control; inclusive premises; trained (experienced) teachers for theoretical and practical training of persons with disabilities; the possibility of studying according to a convenient schedule. Keywords: a driver with a disability, accessibility, driving a vehicle, training.

An Economical Multilevel Backup Strategy for Electro-Hydraulic Braking System by Integrating Driving and Electronic Parking Brake Systems

An Economical Multilevel Backup Strategy for Electro-Hydraulic Braking System by Integrating Driving and Electronic Parking Brake Systems