Advanced Vehicle Technologies Research Articles

Wheels of Change: The Environmental Paradox of Accelerating Vehicle Retirement Program.

Accelerated vehicle retirement is recognized as crucial for managing fleet emissions effectively. However, the emergence of new energy vehicles introduces complexities in assessing their environmental impacts. This study developed a dynamic fleet-based life cycle assessment model to analyze the effects of four distinct scrappage strategies in China from 2021 to 2060. The results underscore the importance of a meticulously chosen retirement strategy that harmonizes fleet characteristics with advancements in vehicle technology to promote sustainable mobility. Accelerated retirement strategies are demonstrated to enhance vehicle sales and expedite the incorporation of new energy vehicles into the fleet. Although these policies reduce greenhouse gas and carbon monoxide emissions, they may exacerbate other air pollutants, necessitating vigilant management. Additionally, the temporal distribution of environmental impacts reveals the critical need for long-term assessments. By evaluating six negative externalities of emissions, the research indicates that targeted scrappage policies, which advocate for the retirement of older vehicles, could potentially generate economic benefits of 6.81 to 7.29 billion dollars in reduced losses. However, an overly aggressive scrappage policy could increase negative externalities, leading to additional costs ranging from 0.84 to 3.31 billion dollars. This study reveals the intricate long-term environmental consequences of scrappage strategies accompanying the rise of new energy vehicles and lays a methodological groundwork for ongoing research into the most effective retirement scheme.

Lightweighting cost impacts on market adoption and GHG emissions in U.S. light-duty vehicle fleet

Abstract Vehicle lightweighting is a promising strategy that can reduce energy consumption and GHG emissions without compromising vehicle’s performance or size. The cost of lightweighting plays a critical role in determining the adoption of lightweighting technologies by consumers and manufacturers among advanced vehicle technologies. This analysis estimates the cost of lightweighting needed to achieve significant light-duty vehicle adoption to provide reductions in GHG emissions. Three different costs of lightweighting scenarios in the U.S. market including a baseline scenario, advanced technology scenario, and widespread scenario are evaluated employing Automotive Deployment Options Projection Tool (ADOPT). ADOPT leverages a database of over 700 existing vehicle models and options, enabling it to provide a high degree of realism and capture the unique characteristics of popular vehicles and the endogenously evolvement of the vehicle options. For baseline scenario, the lightweighting fraction reached by about 15% and GHG emissions are reduced by more than 50% in 2046 compared to 2015. The widespread scenario further reduces the GHG emissions by about 4% from the additional 10% glider mass reduction compared to the baseline scenario. The benefit came largely from lightweighting being implemented in the large market segment of lower-price vehicles, due to the relatively low lightweighting cost ($5/kg).

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

CAR ACCIDENTS IN THE CZECH REPUBLIC AND LIGHT AS A CONTRIBUTORY FACTOR

Ensuring road safety and preventing car accidents are vital concerns for public health and safety. Despite advancements in vehicle technology and traffic management, the number of road accidents remains alarmingly high, leading to significant loss of life and injuries. The study aims to assess the relationship between causes and types of car accidents depending on lighting conditions when an accident happens. The quantitative content analysis of the car accidents recorded by the POLICE between October 2022 and October 2023 provided relevant data, processed by cluster analysis and illustrated via a heat map. Road safety and preventing car accidents have always been a public priority. The authors analysed crucial aspects of traffic safety and alarming numbers of casualties, including 440 deaths, 1,466 badly and 18,389 slightly injured people. The most common types of accidents involved head-on collisions with solid obstacles and ongoing non-rail vehicles. Careless drivers were the main provokers of the tragic events over the monitored period. Looking at the time of accidents, we can see interesting parallels in their causes, irrespective of the day or nighttime. Our recommendations included raising drivers’ awareness and police imposing preventative measures against the common causes of accidents. The study did not consider other factors behind traffic collisions.

Speed Corridor Enforcement System (SCES) - A Case Study of the Highways of Kosovo

Background Traffic safety remains a significant global concern. Despite advancements in vehicle technology, road infrastructure improvements, and initiatives to raise driver awareness, traffic accidents continue to pose socio-economic problems. Objectives The aim of this study was to suggest the potential benefits of implementing a Speed Corridor Enforcement System on highways in Kosovo, aiming to reduce accidents and promote driver awareness. Methods To accomplish the stated objective, there is employed a scientific research methodology supported by surveys, statistics, and analysis methods. The sources of information comprise both primary and secondary data, which were gathered from scientific literature, online sources, and relevant studies in the field. Results The significant socio-economic challenges arising from traffic accidents are discussed, highlighting the necessity for a comprehensive and multidimensional approach to effectively address this issue. Additionally, the proposal of implementing Speed Control Enforcement Systems on the highways of Kosovo, which are part of Intelligent Transport Systems, is emphasized as a solution. Emphasis is placed on the importance of adopting advanced technological solutions alongside comprehensive strategies to effectively tackle the complex problem of traffic safety. Conclusion Considering the alarming 53.41% increase in accidents in Kosovo, the implementation of the speed identification system in corridors could have initially prevented and reduced accidents by a percentage similar to those observed in the reference countries.

Effects of roughness on non-equilibrium turbulent boundary layers

The effects of roughness were considered as part of a NATO Advanced Vehicle Technology effort titled ‘Non-Equilibrium Turbulent Boundary Layers at High Reynolds Numbers’ (NATO AVT-349). This paper comments on the current state of understanding of the flow physics and modelling efforts to predict rough-wall boundary layer behaviour. Outer layer similarity to smooth wall flows and Reynolds number effects are discussed for zero, favourable, and adverse pressure gradients based on the results of experiments and numerical simulations. Various types of modelling are considered including Reynolds averaged Navier-Stokes (RANS) models with different roughness and turbulence models, wall-modelled large eddy simulations (WMLES), and resolvent models. Current needs and gaps in present understanding are discussed along with recommendations for future experiments and computations.

A predictive modeling framework for forecasting cumulative sales of euro-compliant, battery-electric and autonomous vehicles

The automotive industry faces a transformative time with the advent of advanced vehicle technologies and evolving consumer preferences. This research employs predictive modeling to forecast the adoption of Euro 6d and Euro 7 compliant vehicles, Battery Electric Vehicles (BEVs), and Autonomous Vehicles (AVs) in the European Union’s 27 member states and the United Kingdom. This study provides insightful projections and comparative analyses of these technologies’ future trajectories using the modified Gaussian and Logistic models. The modified Gaussian model projected relatively sharp growth curves for all vehicle types, signaling rapid initial adoption followed by saturation. Conversely, the Logistic model depicted more gradual and continuous growth patterns, suggesting sustained market interest over time. Comparative analyses highlight the unique strengths and limitations of each model. The modified Gaussian model proves effective for identifying early market responses and pivotal intervention points, while the Logistic model aids in strategic long-term planning and trend anticipation. However, disparities between the models show the complexity of forecasting automotive market dynamics, emphasizing the need for multifaceted frameworks and approaches. Thus, enhancing predictive accuracy by refining models and integrating additional variables will be pivotal in navigating the dynamic landscape of emerging automotive technologies. This research stands out for its approach to applying analytical methods to predict future market dynamics, offering valuable guidance for policymakers and industry stakeholders in strategizing for the forthcoming technological shift.

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture.

With recent advances in vehicle technologies, in-vehicle networks (IVNs) and wiring harnesses are becoming increasingly complex. To solve these challenges, the automotive industry has adopted a new zonal-based IVN architecture (ZIA) that connects electronic control units (ECUs) according to their physical locations. In this paper, we evaluate how the number of zones in the ZIA affects the end-to-end (E2E) delay and the characteristics of the wiring harnesses. We evaluate the impact of the number of zones on E2E delay through the OMNeT++ network simulator. In addition, we theoretically predict and analyze the impact of the number of zones on the wiring harnesses. Specifically, we use an asymptotic approach to analyze the total length and weight evolution of the wiring harnesses in ZIAs with 2, 4, 6, 8, and 10 zones by incrementally increasing the number of ECUs. We find that as the number of zones increases, the E2E delay increases, but the total length and weight of the wiring harnesses decreases. These results confirm that the ZIA effectively uses the wiring harnesses and mitigates network complexity within the vehicle.

Impacts of Advanced Vehicle Technologies and Risk Attitudes on Distracted Driving Behaviors

Despite concerns over distracted driving, many Americans still engage in risky activities while driving, leading to crashes and fatal outcomes. This study aims to investigate the impact of individual risk attitudes and in-vehicle technologies on various types of distracted driving behaviors (DDB), providing insights into the factors that contribute to an increased likelihood of DDB and enhancing an understanding of the effects of advanced vehicle technologies (AVT) on driver behavior. The analysis leverages self-reported survey questionnaire data from a nationally representative sample of participants. To assess the relationships between the variables, exploratory factor analysis and multiple linear regression analysis were used. The findings revealed that the presence of AVT and individual risk attitudes each predicted DDB. The presence of driver-assist and safety features did, however, lead to some degree of decreased distracted driving. Convenience features, such as Wi-Fi and Bluetooth, were most likely to increase DDB, highlighting the need for the design of AVT systems to minimize distracted driving while leveraging the benefits of technology. The data also indicate that other factors affect DDB. Notably, younger individuals engaged in more DDB compared with older individuals, and individuals who drive more frequently and for longer distances also exhibited a higher frequency of DDB. Factors such as driving experience and exposure also affected DDB, with driving exposure having a more substantial influence.

Research on the optimal speed of vehicles passing speed bumps on the highway based on an immune algorithm

Abstract. With the advancement of vehicle technology, there is a growing demand for vehicle comfort in addition to the focus on safety and functionality. On certain accident-prone sections of highways, such as entrance and exit ramps, tunnels, and downhill stretches, continuous speed bumps are typically installed to remind vehicles to reduce their speed. However, while enhancing safety, these measures also introduce a degree of discomfort for passengers and drivers alike. Vehicle speed and the type of road speed bump are key factors influencing vehicle comfort. In order to improve the ride comfort, this paper investigates the problem of adaptive speed control for vehicles passing over different types of continuous speed bumps and proposes a method for solving the optimal speed. In this research, a 4-degree-of-freedom vehicle suspension model and a road excitation model are employed to simulate vehicle vibrations. Simulation optimisation is performed using MATLAB in conjunction with an immune algorithm to obtain the optimal vehicle speeds for traversing three types of continuous speed bumps – sinusoidal, rectangular, and trapezoidal – while adhering to specified constraints. The simulation results demonstrate that this optimisation algorithm effectively enhances the ride comfort of vehicles when navigating speed bumps. The algorithm, when applied, reduces vehicle vertical displacement, acceleration, suspension deflection, and tyre load to varying degrees when crossing speed bumps. It also reduces tyre ground clearance to some extent, achieving a balance between comfort and safety. Furthermore, the study identifies the range of comfortable vehicle speeds for traversing these three types of speed bumps, providing valuable insights for selecting the appropriate speed bump design on roads with varying speed limits.

An approach for offloading with multi-hop considerations in an RSU signal overlay setting

In recent years, significant advancements in vehicle technology have spurred growing interest in Vehicular Ad hoc Networks (VANETs). This interest is driven by concerns for road safety and the need to alleviate network congestion, leading to the emergence of Intelligent Transport Systems (ITS). ITS focuses on improving road traffic management and safety through the utilization of wireless and mobile network communication technologies. VANETs play a pivotal role within the realm of ITS, facilitating tasks such as enhancing road safety, traffic monitoring, and ensuring passenger comfort by mitigating accidents and congestion. These objectives rely on the timely and accurate delivery of data to vehicle agents and relevant authorities, facilitated by reliable VANETs and Road Signal Units (RSUs). Achieving this necessitates identifying optimal routes with minimal distance, high radio access, and quality-awareness levels. To address these objectives, this study proposes the utilization of the Congestion Network with Predicted K-means multi-hop RSU algorithm (CN-MHMR) to enhance vehicular networking and communication. This algorithm facilitates efficient node transfer from base nodes to destination nodes via the shortest and energy-efficient paths, thereby enabling viable and reliable vehicular communications. The performance of the proposed model was evaluated based on various metrics, including energy consumption, throughput, delay, packet delivery ratio, accuracy, precision, and recall values.

Advancements and Hurdles in the Evolution of Automotive Wireless Interfaces: A Technical Exploration

The integration of wireless interfaces into vehicles has posed some challenges for the automotive industry over the years. While manufacturers strive to impress consumers with cutting-edge features, these features also bring security risks that cannot be ignored. To prevent potentially fatal incidents, a thorough protocol must be established to address system vulnerabilities. As the modern century moves towards an era of autonomous vehicles, security must be a top priority to avoid compliance breaches and delays in feature development. The significance of vehicle interfaces in the modern automotive industry cannot be overstated. The present study aims to explore the prospective advantages and challenges associated with the integration of wireless interfaces in the automotive industry. This analysis will primarily focus on the latest technological advancements in vehicle technology and the critical need to secure against possible cyber-attacks. A wide range of topics will be covered in this paper, from the evolution of vehicle interfaces to the industry’s hurdles and strategies to minimize the risks associated with cyber threats. The objective of this study is to provide a comprehensive understanding of wireless interfaces in the automotive sector, including the benefits of implementing such technology, the challenges that it poses, and the measures needed to maintain the security and safety of vehicles, as well as the passengers.

Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes

Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving in these vehicle categories, shedding light on the variations in single-vehicle crashes. Focusing on single-vehicle crashes in Florida during 2019, involving passenger cars, SUVs, pickup trucks, and minivans caused by distracted driving, the study examines various distractions such as, electronic communication devices (cell phones), electronic devices (navigation systems, music players), internal and external disturbances, texting, and inattentive driving. To analyze the severity of injuries resulting from distracted driving in passenger cars, SUVs, pickup trucks, and minivans, the study employs random parameter multinomial logit models with heterogeneity in means and variances. The model estimates highlight thirty-five significant factors influencing the severity of driver injuries resulting from distracted driving. Notably, the impact of these factors varies significantly depending on the vehicle type (i.e., passenger cars, SUVs, pickup trucks, and minivans). While many explanatory variables are specific to each vehicle type, only one factor (restraint belt usage) is common across all vehicle types, with varying magnitudes in injury outcomes. The likelihood ratio tests indicate that injury severity must be analyzed and modeled separately for passenger cars, SUVs, pickup trucks, and minivans. Vehicle characteristics play a crucial role in driver distraction and crash outcomes. Analyzing a year of crash data, categorized by four vehicle types, has provided valuable insights into distracted driving patterns in passenger cars, SUVs, pickup trucks, and minivans, influencing potential prevention strategies. To combat against distracted driving effectively, priority should be given to driver education and training, roadway design, vehicle technology, enforcement, and automobile insurance. The automobile industry, especially for passenger cars, SUVs, pickup trucks, and minivans, should consider implementing advanced in-vehicle technologies tailored to the specific characteristics of each vehicle type (e.g., advanced driver assistance systems (ADAS)) to proactively prevent driver distraction. These proactive measures will contribute significantly to enhancing road safety and reducing the risks associated with distracted driving.

Design and Simulation of Micro-Electro-Mechanical Systems (MEMS) Capacitive Pressure Sensor for Thermal Runaway Detection in the Electric Vehicle

Recent advancement of vehicle technologies has resulted in development of replacing conventional Internal combustion engine (ICE) to Electric Vehicle (EV) mostly powered by Lithium-ion batteries (LIB). These batteries contain massive amount of energy confined in a very small space. Thermal runaway occurs when the batteries and its circuits start to heat up anomaly. Thermal runaway can cause failures that can lead to battery ignition, resulting in explosions and imminent threats to life and property. This research focused on MEMS capacitance pressure sensor, using three distinct square slotted diaphragm designs: clamped-square, four-slotted-square, and eight-slotted-square diaphragms. The investigation commenced with an evaluation of diaphragm performance, and subsequently, the diaphragm was integrated into the structure of the MEMS capacitive pressure sensor and subjected to simulation. Varied pressure levels ranging from 0.1 to 0.35 MPa were applied to both the diaphragm and the pressure sensor. The outcomes revealed that the eight-slotted-square diaphragm yielded the most substantial displacement, registering at 5.507 µm. It also exhibited the highest Mises stress of 644.67 MPa, and recorded the highest mechanical sensitivity at 15.7545 (10-12/Pa). The clamped-square design, despite its slotted area, yielded the highest capacitance value among the three designs for the pressure sensor.

A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data

On freeways, sudden deceleration or lane-changing by vehicles can trigger conflict risk that propagates backward in a specific pattern. Simulating this pattern of conflict risk propagation can not only help prevent crashes but is also vital for the deployment of advanced vehicle technologies. However, conflict risk propagation simulation (CRPS) on freeways is challenging due to the nuanced nature of the pattern, intricate spatio-temporal interdependencies among sequences and the high-resolution requirements. In this work, we introduce a conflict risk index to delineate potential conflict risk by aggregating various surrogate safety measures (SSMs) over time and space, and then propose a Spatio-Temporal Transformer Network (STTN) to simulate its propagation patterns. Multi-head attention mechanism and stacking layers enable the transformer to learn dynamic and hierarchical features in conflict risk sequences globally and locally. Two components, spatial and temporal learning transformers, are innovatively incorporated to extract and fuse these features, culminating in a fine-grained conflict risk inference. Comprehensive tests in real-world datasets verified the effectiveness of the STTN. Specifically, we employ three widely-recognized SSMs: Modified Time-To-Collision (MTTC), Proportion of Stopping Distance (PSD), and Deceleration Rate to Avoid a Collision (DRAC). These SSMs, gleaned from vehicle trajectories, are employed to delineate the conflict risk. Then, we conduct three comparative simulation tasks: MTTC-based model, PSD-based model, and DRAC-based model. Experimental results show that the PSD-based model exhibits a robust performance on all tasks, and is minimally affected by the durations of the simulation time, while the DRAC-based model more distinctly delineates the spatio-temporal conflict risk heterogeneity. Furthermore, we benchmark the STTN against three common state-of-the-art machine learning models across all simulation tasks. Results reveal that the STTN consistently surpassed these benchmark models (LSTM, CNN and ConvLSTM), suggesting the potential of the attention mechanism on the CRPS tasks. Our investigation offers crucial insights beneficial for traffic safety warning, advanced freeway management systems, and driver assistance systems, among others.

Environmental Sustainability and Freight Transport Performance in the EU – An Autoregressive Conditional Heteroscedasticity (ARCH) Model Analysis

Transportation, which is a significant facilitator of global trade and development, faces a serious problem with respect to sustainability. Firstly, there is the need to minimise greenhouse gas emissions while maintaining profitability and social responsibility. Transportation will be totally decarbonised by consistently moving towards a more sustainable, diverse and resilient range of transportation modes with advanced vehicle technologies. However, what impact this will have on the economic performance of transport service providers remains a big question. The aim of this study is to examine the short-run relationship between environmental sustainability in road freight transportation and the economic performance of the road freight transport sector in the European Union using an autoregressive conditional heteroscedasticity (ARCH) model. The analysis was conducted using annual data spanning from 2008 to 2021. The results indicate that energy taxes on transport and storage, biodiesel consumption and vehicle capacity utilisation have a positive and significant impact on freight transport performance (FTP). The findings suggest that policymakers could use energy taxes and incentives to promote the use of biodiesel in the transportation sector to increase FTP. Additionally, efforts to improve vehicle capacity utilisation could significantly increase FTP and have positive environmental implications such as reducing traffic congestion and emissions.

Risk prediction for cut-ins using multi-driver simulation data and machine learning algorithms: A comparison among decision tree, GBDT and LSTM

The cut-ins (one kind of lane-changing behaviors) have result in severe safety issues, especially at the entrances and exits of urban expressways. Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences. This paper makes some efforts on these purposes. In this paper, twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection. The surrogate measures, Time Exposure Time-to-Collision (TET) and Time Integrated Time-to-collision (TIT) were employed to quantify the risk of cut-ins, then k-means clustering was applied for risk classification of 3 levels. Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables. Based on these variables, three prediction models including decision tree (DT), gradient boosting decision tree (GBDT) and long short-term memory (LSTM) are used for predicting the risks of cut-ins. Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data. From results of risk prediction models, the LSTM, with an overall accuracy of 87%, outperforms the GBDT (80.67%) and DT (76.9%). Despite this, this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.

The Relationship Between Partial-Automation Use and Driver Sentiment Toward Automation

In this study we utilize naturalistic driving data and semi-structured interviews from the MIT Advanced Vehicle Technology naturalistic driving study to explore the relationship between partial-automation use behaviors and driver sentiment by connecting automation use, prevalence of Elevated Driving Kinematic (EDK) events, and incidence of drops in steering support, with participant sentiments towards partial-automation performance and trust in partial-automation. We found that higher automation users expressed more positive statements about system use ( p = 0.012). Additionally, participants that experienced more EDKs during automation use expressed lower sentiments concerning automation performance. Participants that experienced more drops in automated steering support expressed lower trust in automation and lower sentiments toward automation performance. This work takes an important step in connecting driver sentiment to observed and measurable behaviors in real-world driving to aid in design of more useful and trustworthy automation.

Experimental study and modeling of rubber joints for railway vehicles using magnetorheological shear stiffening elastomers

With the rapid development of transportation industry, advanced rail vehicle technology receives more attention than ever. The stiffness of the train’s rubber joint at the primary suspension system has a crucial influence on the operation stability and curve-passing performance. When the train is running on straight track at high speed, a high primary longitudinal stiffness in bogie design is required, whereas running on the curve track calls for a soft primary longitudinal stiffness. To solve this critical problem, a new rubber joint based on magnetorheological shear stiffening elastomer (MSSE) was proposed. Its stiffness can be adjusted by not only external magnetic field but also its inherent frequency-dependent property, ensuring the functionality of the rubber joint even when the controller fails. The prototype of the MSSE joint was fabricated and assembled. Stiffness controllability of the MSSE joint was evaluated using an material testing system (MTS) machine, with MTS testing performed under varying displacement amplitude at fixed frequency to investigate the influence of the varying displacement amplitude on the effective stiffness. The results revealed that the stiffness of this MSSE joint can be controlled effectively credited to the rate-dependent SSE and adjustable electromagnetics, exhibiting exceptional fail-safe characteristics. Lastly, a dynamic model was established to describe the dynamic performance of the rubber joint. All the above studies demonstrate the feasibility of the joint to satisfy the conflicting stiffness requirements to achieve high speed stability and curve trafficability simultaneously.

Road Condition Monitoring Using Vehicle Built-in Cameras and GPS Sensors: A Deep Learning Approach

Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads’ conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles’ built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions.