Advanced Driver Assistance Systems Technologies Research Articles

Evaluating the User Experience of V2X Communication Alerts for Emergencies in Level 3 Autonomous Driving

Communicating effectively with autonomous vehicles requires contextualized visual and auditory cues to ensure clear message delivery. Evaluating the user experience involves assessing which types of information can be safely reacted to without additional monitoring and how it is presented. Validating the visual and auditory cues supports the driver’s course of action. This study investigates message types and preferred modalities of driver-to-driver communication via vehicle-to-everything (V2X) in advanced driver assistance systems technologies for autonomous and manual driving and proposes efficient ways to respond to event situations. Four modalities, including baseline and three message types with different information were proposed to investigate the information required by drivers. Results indicate that providing notifications during autonomous driving is more helpful and less workload-intensive than during manual driving. Most notifications were highly visible and easy to recognize. Although behavioral messages in both autonomous and manual driving enhance usability, providing advice and behavioral messages is safer for autonomous driving. Designing V2X notification information based on future events is vital because of its highly pragmatic nature.

Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection

The recent advancements in Advanced Driver Assistance Systems (ADAS) have significantly contributed to road safety and driving comfort. An integral aspect of these systems is the detection of driver anomalies such as drowsiness, distraction, and impairment, which are crucial for preventing accidents. Building upon previous studies that utilized ensemble model learning (XGBoost) with deep learning models (ResNet50, DenseNet201, and InceptionV3) for anomaly detection, this study introduces a comprehensive feature importance analysis using the SHAP (SHapley Additive exPlanations) technique. The technique is implemented through explainable artificial intelligence (XAI). The primary objective is to unravel the complex decision-making process of the ensemble model, which has previously demonstrated near-perfect performance metrics in classifying driver behaviors using in-vehicle cameras. By applying SHAP, the study aims to identify and quantify the contribution of each feature – such as facial expressions, head position, yawning, and sleeping – in predicting driver states. This analysis offers insights into the model’s inner workings and guides the enhancement of feature engineering for more precise and reliable anomaly detection. The findings of this study are expected to impact the development of future ADAS technologies significantly. By pinpointing the most influential features and understanding their dynamics, a model can be optimized for various driving scenarios, ensuring that ADAS systems are robust, accurate, and tailored to real-world conditions. Ultimately, this study contributes to the overarching goal of enhancing road safety through technologically advanced, data-driven approaches.

Retrofitting pedestrian and cyclist collision warning for improved safety of heavy trucks employed in urban areas: a case study in Florence and Pistoia, Italy

The increasing use of heavy vehicles for services such as public transport, waste collection and logistics in densely populated urban areas causes numerous collisions with serious consequences every year, particularly for pedestrians, cyclists and motorcyclists. Advanced Driver Assistance Systems (ADAS) have proven to be a solution because they can significantly reduce the occurrence and consequences of road crashes. Although many ADAS technologies will be mandatory on new trucks in 2024, it will take more than a decade before the benefits are realised due to the average fleet renewal time. To accelerate this process, the most promising solution is therefore the introduction of aftermarket ADAS (also referred to as ‘retrofit’ ADAS) on the circulating fleet. This study aims to identify a tailored solution for retrofitting ADAS capable of providing pedestrian collision warnings on heavy vehicle fleets currently circulating in urban areas. The study involves a sample of vehicles and drivers operating in the metropolitan areas of Florence and Pistoia for waste collection services. The company owner of the vehicles integrated a commercial camera-based ADAS system with three additional cameras installed on the frontal bumper and edges of the vehicle, linked with three screens positioned in the vehicle cabin. In this study, a field test campaign was performed integrating trials in a proving ground closed to traffic with testing and monitoring in urban areas. This allowed to define a set of recommended parameters depending on vehicle speed that represent a good balance between alert frequency, minimizing false positives and ensuring that the alerts indicate real potential hazards to the driver. The recommendations provided in this study can also be extended to other vehicle fleets so that rapid and substantial improvements in the safety of heavy vehicles operating in cities can be achieved.

A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these problems. To manage higher-level connectivity and automation, the paper analyses and implements key research. It also finds issues and recommends solutions. The latest driverless car ADAS improvements have dramatically increased passenger safety. These systems are safer and more automated using sensors and ECUs. Most ADAS have RADAR, cameras, ultrasonic, and LiDAR. This work uses AI/ML-enabled Predictive Maintenance modelling to improve ADAS safety and longevity. AI and ML in Advanced Driver Assistance Systems (ADAS) are significant vehicle safety and reliability advances. AI/ML-enabled predictive maintenance detects and fixes ADAS component faults. ADAS predictive maintenance using AI/ML can detect issues, improve driver safety, and boost vehicle efficiency. Advanced sensor arrays and control units are needed for adaptive cruise control, traffic sign recognition, and lane-keeping assistance. AI/ML algorithms discover issues and enable early interventions in predictive maintenance models. Predictive maintenance is examined utilizing classical machine learning, deep learning, and reinforcement learning. Integration of numerous AI/ML models, real-time data processing, customization based on vehicle usage patterns, scalability, and adaptability of predictive maintenance models to new ADAS technologies are research gaps.

Proactive Braking Control System for Collision Avoidance during Right Turns with Occluded Vision at an Intersection

This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one of the commercialized ADAS technologies for collision avoidance. However, the coverage of the ADAS will be limited to designated intersections only, as communication equipment needs to be installed in both the vehicle and infrastructure to enable the assistance. This paper proposes an ADAS using on-board sensors, independent of infrastructure facilities, to control the vehicle velocity to avoid collisions. Most current intersection assistance, by using an Autonomous Emergency Braking System (AEBS), allows the driver to avoid a collision with oncoming vehicles when there is clear vision without occlusion. However, many accidents occur when the vehicle detects the oncoming vehicle too late because of occlusion in the intersection, such as a vehicle in the opposite lane. This system calculates the hazardous speed criteria of the ego vehicle, which might result in a high risk of collision when darting out occurs, and provides speed control assistance to allow the driver to escape from the hazardous speed region. The simulation results reveal that the proposed system effectively reduces the possibility of collisions compared to conventional AEBS.

Does training improve users' mental models about adaptive cruise control?

While Advanced Driver Assistance Systems (ADAS) promise safety benefits to drivers, there is evidence to suggest that drivers are unaware or uninformed about their vehicles’ systems and thus have poor mental models about the systems. Previous studies suggest that training improves drivers’ mental models, although some studies report limited impacts. This study investigated the relationship between training and drivers’ mental models about Adaptive Cruise Control (ACC), compared the impact of two different training approaches on drivers’ mental models, and examined the relationship between driver knowledge and trust regarding ADAS technologies. This study was conducted online, and participants were randomly and equally assigned to one of three training groups – owner’s manual (text-based); state diagram visualization; or sham (control). Surveys measured their trust and mental models about ACC before and after training. The results found that the text-based group outperformed the visualization group and the control group in terms of post-training overall mental model scores, but these differences were not statistically significant. No correlation between post-training mental model scores and overall trust scores was found. This study provides evidence that training improves users’ mental models about technology and finds that different training platforms or paradigms may affect learning differently.

UTILIZING ADVANCED DRIVER ASSISTANCE SYSTEMS IN LATER LIFE: INSIGHTS FROM A LONGITUDINAL STUDY ON AGING DRIVERS

Abstract Level 1 and 2 Advanced Driver Assistance System (ADAS) technologies (e.g., adaptive cruise control, blind spot warning, lane keep assist) are predicted to be available in approximately three-fourths of all vehicles worldwide by 2025. To realize the potential of these systems in extending safe mobility for aging adults, it is critical that the driver understands the functionality of the technology and uses the system appropriately. The current study examined demographic and vehicle technology questionnaire data from the Longitudinal Research on Aging Drivers (LongROAD) cohort study which concluded in December 2022. A total of 1,417 participants changed their vehicle throughout the study. There were statistically significant increases in the prevalence of all 15 ADAS technologies examined. Despite increases in prevalence, frequency of using the technologies remained unchanged across 5 years. Frequency of use also varied by functionality of the technology whereby participants reported higher frequency of using technologies that provide alerts, such as blind spot warning, than technologies that take action to assist drivers with vehicle operations, such as adaptive cruise control. Results showed differences in prevalence and use of technologies by income and education, suggesting disparities in access to vehicles with technologies that could help to create a safer driving experience. In consideration of the rapid proliferation of ADAS into the vehicle fleet, increased research into how older drivers learn about and use ADAS technologies will assist in efforts to develop tailored and accessible programs for training older adults to properly utilize ADAS available in their own vehicles.

Exploring perceptions of Advanced Driver Assistance Systems (ADAS) in older drivers with age-related declines

Perceptions of Advanced Driver Assistance Systems (ADAS) were explored in two semi-structured face-to-face focus group studies of 42 older drivers (aged 65 years and older) with and without age-related declines. Study 1 explored perceptions regarding ADAS, focusing on visual, auditory, physical, and cognitive factors. Study 2 extended this by additionally exploring perceptions following exposure to videos and stationary vehicle demonstrations of an ADAS. Participants had a range of visual, hearing, memory, and health characteristics which impacted on their daily life. In both studies, some participants had insights regarding various ADAS technologies prior to the study, but many were unfamiliar with these systems. Nevertheless, overall, participants reported that ADAS would assist them to drive as they age and increase their mobility and independence. There were comments regarding the benefits of warning alerts, although the potential for them to be distracting was also highlighted. Participants with vision impairment preferred audio alerts and participants with hearing impairment preferred visual display alerts. Findings highlighted the potential for ADAS to assist those with age-related declines and the need to increase the flexibility of warning system alerts to suit the varying requirements of older drivers, as well as to reduce the complexity of vehicle interfaces. Collectively, these strategies would maximize the benefits of these vehicles to increase the mobility, independence, and quality of life of older drivers with and without age-related declines.

Driving Difficulties and Preferences of Advanced Driver Assistance Systems by Older Drivers With Central Vision Loss.

The purpose of this study was to investigate driving difficulties and Advanced Driver Assistance Systems (ADAS) use and preferences of drivers with and without central vision loss (CVL). Fifty-eight drivers with CVL (71 ± 13 years) and 68 without (72 ± 8 years) completed a telephone questionnaire. They rated their perceived driving difficulty and usefulness of technology support in 15 driving situations under good (daytime) and reduced visibility conditions, and reported their use experience and preferences for 12 available ADAS technologies. Drivers with CVL reported more difficulty (P = 0.002) and greater usefulness of technology support (P = 0.003) than non-CVL drivers, especially in reduced visibility conditions. Increased driving difficulty was associated with higher perceived technology usefulness (r = 0.34, P < 0.001). Dealing with blind spot road users, glare, unexpected pedestrians, and unfamiliar areas were perceived as the most difficult tasks that would benefit from technology support. Drivers with CVL used more advanced ADAS features than non-CVL drivers (P = 0.02), preferred to own the blind spot warning, pedestrian warning, and forward collision avoidance systems, and favored ADAS support that provided both information and active intervention. The perceived benefits of and willingness to own ADAS technologies were high for both groups. Drivers with CVL used more advanced ADAS and perceived greater usefulness of driver assistance technology in supporting difficult driving situations, with a strong preference for collision prevention support. This study highlights the specific technology needs and preferences of older drivers with CVL, which can inform future ADAS development, evaluation, and training tailored to this group.

Preparing drivers for the future: Evaluating the effects of training on drivers’ performance in an autonomous vehicle landscape

The rapid development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technology has raised concerns about the ability of ordinary drivers to safely interact with these systems without formal training or practice sessions. This study investigates the impact of training on drivers' ability to interact with ADAS functions, aiming to enhance road safety and prepare drivers for future autonomous driving. To do so the study compared novice and experienced drivers' performance across various driving training sessions separated by different time intervals. Using a simulation-based driving scenario, a diverse group of participants with different ages, genders, driving experience, and ADAS usage frequencies was recruited. Participants were instructed to drive a virtual subject vehicle (SV) multiple times with different time intervals and various ADAS functions being activated and/or deactivated. Data on steering wheel input, acceleration, braking, event triggering time, response time, and vehicle lateral position were collected and analysed. Participants' performance, measured in terms of accuracy, reaction time in interacting with ADAS functions, and vehicle control when they are interacting with those functions, was assessed after each training session. Results revealed significant improvements in accuracy, reaction time, and vehicle control for both novice and experienced drivers' performance following training. The results highlight the importance of appropriate training for drivers to safely operate ADAS and autonomous functions, providing valuable insights for policymakers, vehicle manufacturers, and driver education institutions. Furthermore, the findings emphasise the need for further investigations on the effectiveness of various training modalities and the potential benefits of integrating real-time feedback systems or Artificial Intelligence (AI)-assisted coaching. This study has important ramifications for the future design and implementation of driver training programs, as well as the development of policies and regulations to promote the safe use of ADAS and AV technology in the future.

Beyond adaptive cruise control and lane centering control: drivers' mental model of and trust in emerging ADAS technologies.

The potential safety benefits of advanced driver assistance systems (ADAS) highly rely on drivers' appropriate mental models of and trust in ADAS. Current research mainly focused on drivers' mental model of adaptive cruise control (ACC) and lane centering control (LCC), but rarely investigated drivers' understanding of emerging driving automation functions beyond ACC and LCC. To address this research gap, 287 valid responses from ADAS users in the Chinese market, were collected in a survey study targeted toward state-of-the-art ADAS (e.g., autopilot in Tesla). Through cluster analysis, drivers were clustered into four groups based on their knowledge of traditional ACC and LCC functions, knowledge of functions beyond ACC and LCC, and knowledge of ADAS limitations. Predictors of driver grouping were analyzed, and we further modeled drivers' trust in ADAS. Drivers in general had weak knowledge of LCC functions and functions beyond ACC and LCC, and only 27 (9%) of respondents had a relatively strong mental model of ACC and LCC. At the same time, years of licensure, weekly driving distance, ADAS familiarity, driving style (i.e., planning), and personability (i.e., agreeableness) were associated with drivers' mental model of ADAS. Further, it was found that the mental model of ADAS, vehicle brand, and drivers' age, ADAS experience, driving style (i.e., focus), and personality (i.e., emotional stability) were significant predictors of drivers' trust in ADAS. These findings provide valuable insights for the design of driver education and training programs to improve driving safety with ADAS.

Evaluation of Fuel Economy Benefits of Radar-Based Driver Assistance in Randomized Traffic

&lt;div&gt;Certain advanced driver assistance systems (ADAS) have the potential to boost energy efficiency in real-world scenarios. This article details a radar-based driver assistance scheme designed to minimize fuel consumption for a commercial vehicle by predictively optimizing braking and driving torque inputs while accommodating the driver’s demand. The workings of the proposed scheme are then assessed with a novel integration of the driver assistance functionality in randomized traffic microsimulation. Although standardized test procedures are intended to mimic urban and highway speed profiles for the purposes of evaluating fuel economy and emissions, they do not explicitly consider the interactions present in real-world driving between the ego vehicle equipped with ADAS and other vehicles in traffic. This article presents one approach to address the drawback of standardized test procedures for evaluating the fuel economy benefits of ADAS technologies. This approach is demonstrated by using a microsimulation of a traffic network into which the ego vehicle with the proposed driver assistance scheme is embedded for continuous interaction with the traffic. The analysis and results from stochastic simulations consider variations in the behavioral style of the driver of the ego vehicle and the traffic density. Strong variations, up to 10% in the fuel economy benefits, are observed between both variations presented in this study and what is obtained in typical deterministic evaluations mirroring standard test procedures.&lt;/div&gt;

Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

The increasing number of vehicles has caused traffic conditions to become increasingly complicated in terms of safety. Emerging autonomous vehicles (AVs) have the potential to significantly reduce crashes. The advanced driver assistance system (ADAS) has received widespread attention. Lane keeping and lane changing are two basic driving maneuvers on highways. It is very important for ADAS technology to identify them effectively. The lane changing maneuver recognition has been used to study traffic safety for many years. Different models have been proposed. With the development of technology, machine learning has been introduced in this field with effective results. However, models which require a lot of physical data as input and unaffordable sensors lead to the high cost of AV platforms. This impedes the development of AVs. This study proposes a model of lane changing maneuver recognition based on a distinct set of physical data. The driving scenario from the natural vehicle trajectory dataset (i.e., HighD) is used for machine learning. Acceleration and velocity are extracted and labeled as physical data. The normalized features are then input into the k-nearest neighbor (KNN) classification model. The trained model was applied to another set of data and received good results. The results show that based on the acceleration features, the classification accuracy of lane keeping (LK), lane changing to the left (LCL) and lane changing to the right (LCR) is 100%, 97.89% and 96.19%.

A deep learning approach for lane marking detection applying encode-decode instant segmentation network

A lot of people suffer from disability and death due to unintentional road accidents, which also result in the loss of a significant amount of financial assets. Several essential features of Advanced Driver Assistance Systems (ADAS) are being incorporated into vehicles by researchers to prevent road accidents. Lane marking detection (LMD) is a fundamental ADAS technology that helps the vehicle to keep its position in the lane. The current study employs Deep Learning (DL) methodologies and has several research constraints due to various problems. Researchers sometimes encounter difficulties in LMD due to environmental factors such as the variation of lights, obstacles, shadows, and curve lanes. To address these limitations, this study presents the Encode-Decode Instant Segmentation Network (EDIS-Net) as a DL methodology for detecting lane marking under various environmental situations with reliable accuracy. The framework is based on the E-Net architecture and incorporates combined cross-entropy and discriminative losses. The encoding segment was split into binary and instant segmentation to extract information about the lane pixels and the pixel position. DenselyBased Spatial Clustering of Application with Noise (DBSCAN) is employed to connect the predicted lane pixels and to get the final output. The system was trained with augmented data from the Tusimple dataset and then tested on three datasets: Tusimple, CalTech, and a local dataset. On the Tusimple dataset, the model achieved 97.39% accuracy. Furthermore, it has an average accuracy of 97.07% and 96.23% on the CalTech and local datasets, respectively. On the testing dataset, the EDIS-Net exhibited promising results compared to existing LMD approaches. Since the proposed framework performs better on the testing datasets, it can be argued that the model can recognize lane marking confidently in various scenarios. This study presents a novel EDIS-Net technique for efficient lane marking detection. It also includes the model's performance verification by testing in three different public datasets.

Blind Spot and Drowsiness Detection for Intelligent Transportation System

This research paper investigates two critical aspects of Advanced Driver Assistance Systems (ADAS): Blind Spot Detection (BSD) and Drowsiness Detection (DD). Our primary objective is to assess these ADAS features, emphasizing their efficacy, advantages, and constraints. SD relies on sensors and cameras to monitor blind spots, alerting drivers to potential lane change risks. DD employs sensors and algorithms to detect signs of driver drowsiness or fatigue, issuing timely warnings. This study analyzes technological innovations in BSD and DD, examining their integration into vehicles and their potential to mitigate accidents caused by blind spots and drowsy driving. We also explore human factors, including user acceptance and usability concerns. Additionally, we assess the real-world impact of BSD and DD on road safety, utilizing data from studies and accident reports to quantify their effectiveness. This research contributes to discussions on ADAS technologies, emphasizing their role in enhancing road safety. We aim to encourage further development and integration of these systems into the automotive industry, ultimately leading to safer transportation for all.

Feasibility of automated in-vehicle technologies on volunteers' driving performance.

Automated in-vehicle technologies, specifically in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), are increasingly common in today's cars. Previous studies illustrate benefits of using IVIS and ADAS to improve safety, convenience, and comfort in healthy older drivers. However, research is sparse on the feasibility of such technologies for medically at-risk drivers, such as those with Parkinson's disease (PD). This study enrolled healthy volunteer drivers to examine the feasibility of the procedures and measures for evaluating the effects of IVIS and ADAS on their driving performance. During this feasibility study researchers compared drives completed with and without support of IVIS and ADAS, as participants drove a 2019 Toyota Camry XLE. The test vehicle was equipped with IVIS, ADAS, cameras, a telematics system, and sensors. Participants drove the road course supervised by a Driver Rehabilitation Specialist (DRS). Overall study procedures and vehicle equipment were feasible and provided sufficient data collection for measuring the impact of IVIS and ADAS on driving performance. Data observation by the DRS combined with data captured from cameras and telematics, facilitated comparisons to increase data reliability and validity. Feasibility study findings informed a randomized clinical trial, examining the use of IVIS and ADAS technologies as an intervention to support drivers with Parkinson's disease.

Longer-term exposure vs training: Their effect on drivers' mental models of ADAS technology

Drivers often learn about the advanced driver assistance systems (ADAS) on their vehicles over time and through trial and error. While this experience can aid drivers’ understanding about the systems, it may not necessarily lead to sufficient and accurate mental models, especially concerning less frequent “edge case” situations. This study recruited 39 new owners of vehicles equipped with ADAS technology to which the owners were naïve. The initial mental model of these owners was evaluated using a mental model assessment. To understand changes in mental models over time the assessment was repeated six times over the course of approximately 6 months. Weekly mileage, technology usage, and information regarding their exposure to edge case scenarios was also collected. At the end of the 6 months, participants completed a simulator drive using adaptive cruise control (ACC) that included several edge cases. Over the course of the first 6 months of vehicle ownership, drivers’ scores on the mental model assessment improved. These improvements were largely due to increased understanding of the technology’s limitations as opposed to improvements in knowledge about system function. With respect to driving performance in the simulator session, the mental model scores were not predictive of responses to the edge cases. However, a comparison of those mental model scores against weak and strong mental model benchmark scores gathered in a previous study revealed that mental models improve over 6 months (for some drivers), but not to the level of understanding of a group that received a short but extensive introduction to ACC. This suggests that there is room for improvement in how drivers gain understanding about driver support features and further underscores the need of training and education for proper use and interactions.

Law enforcement officers’ acceptance of advanced driver assistance systems: An application of technology acceptance modeling (TAM)

Vehicle crashes are one of the main causes of injuries and deaths for law enforcement officers (LEOs) in the line of duty. These crashes occur due to factors such as driving at high speed in emergency situations, fatigue, or use of in- vehicle technologies while driving. Advanced driver assistance systems (ADAS) have the potential to reduce crashes and improve LEO safety in police operations; however, there has been no prior research on acceptance of these technologies among LEOs. A survey study with 73 LEOs was conducted to understand factors that affect their acceptance and intention to use ADAS technologies. Results suggested that trust and training of ADAS features can significantly increase LEOs’ intention to use the technologies. The findings can provide guidelines to enhance LEOs’ attitude and acceptance towards ADAS.

An unsupervised learning framework for detecting adaptive cruise control operated vehicles in a vehicle trajectory data

The traffic dynamics are expected to change with the widespread utilization of advanced driver assistance systems (ADAS). Currently, simulation tools are adopted to capture the impacts of ADAS technologies on traffic dynamics. Real-world data collection of different ADAS technologies is required to support realistic modeling of these technologies in simulation tools. Vehicle trajectories are one of the cornerstones of modern traffic flow theory with applications in driver behavior studies and automated vehicle (AV) research. Unfortunately, the current trajectory datasets fail to provide any information on the utilization of ADAS technologies. This study proposes collecting and using a new trajectory dataset that contains multiple instances of probe vehicles using adaptive cruise control (ACC) to identify ACC-type behavior across the entire trajectory dataset. Since the trajectory data is not labeled based on ACC utilization, clustering is an excellent approach to arrange similar trajectories in the dataset into the same group. Using this dataset combined with clustering, this study identifies the vehicle trajectories with similar dynamics to the vehicles using ACC.

ObjectDetect: A Real-Time Object Detection Framework for Advanced Driver Assistant Systems Using YOLOv5

In the recent times, there has been a lot of speculation related to advanced driver-assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as R-CNN, Fast R-CNN, and YOLO were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called “A Real-Time Object Detection Framework for Advanced Driver Assistant Systems” by implementing the state-of-the-art object detection algorithm YOLOv5 which improves the speed in detection of object over real-time. This paper provides a comparison between other state-of-the-art object detectors such as YOLOv3 and YOLOv4. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLOv5 proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called “ObjectDetect” which helps users make better decisions on the road. “ObjectDetect” consists of a simple user interface that displays alerts and warnings.