Browse Topic: Driver behavior
Road safety remains a critical concern globally, with millions of lives lost annually due to road accidents. In India alone, the year 2021 witnessed over 4,12,432 road accidents resulting in 1,53,972 fatalities and 3,84,448 injuries. The age group most affected by these accidents is 18-45 years, constituting approximately 67% of total deaths. Factors such as speeding, distracted driving, and neglect to use safety gear increases the severity of these incidents. This paper presents a novel approach to address these challenges by introducing a driver safety system aimed at promoting good driving etiquette and mitigating distractions and fatigue. Leveraging Raspberry Pi and computer vision techniques, the system monitors driver behavior in real-time, including head position, eye blinks, mouth opening and closing, hand position, and internal audio levels to detect signs of distraction and drowsiness. The system operates in both passive and active modes, providing alerts and alarms to the
The integration of Vehicle-to-Everything (V2X) communication technologies holds immense potential to revolutionize the automotive industry by enabling vehicles to communicate with each other (V2V) and with infrastructure (V2I). This paper investigates the feasibility of V2X and V2I communication, exploring available communication methods for vehicles to communicate. Many a times people like to travel together and it involves more than one vehicle travelling together, in such cases they often get lost the information about fellow vehicles due to the traffic condition and different driving behaviors of the individual driver. In such cases they communicate over phones to get to know the location of fellow vehicle or keep sharing their live locations. In such cases they don’t just follow the destination in maps also they should be continuously monitoring their fellow vehicles position. It is important for vehicles travelling in group to have communication and be connected so that they know
The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering. The basis for this is the route generation as a result of the evaluation of curve radii from several hundred thousand kilometers of real measurement
iMotions employs neuroscience and AI-powered analysis tools to enhance the tracking, assessment and design of human-machine interfaces inside vehicles. The advancement of vehicles with enhanced safety and infotainment features has made evaluating human-machine interfaces (HMI) in modern commercial and industrial vehicles crucial. Drivers face a steep learning curve due to the complexities of these new technologies. Additionally, the interaction with advanced driver-assistance systems (ADAS) increases concerns about cognitive impact and driver distraction in both passenger and commercial vehicles. As vehicles incorporate more automation, many clients are turning to biosensor technology to monitor drivers' attention and the effects of various systems and interfaces. Utilizing neuroscientific principles and AI, data from eye-tracking, facial expressions and heart rate are informing more effective system and interface design strategies. This approach ensures that automation advancements
With further development of autonomous vehicles additional challenges appear. One of these challenges arises in the context of mixed traffic scenarios where automated and autonomous vehicles coexist with manually operated vehicles as well as other road users such as cyclists and pedestrians. In this evolving landscape, understanding, predicting, and mimicking human driving behavior is becoming not only a challenging but also a compelling facet of autonomous driving research. This is necessary not only for safety reasons, but also to promote trust in artificial intelligence (AI), especially in self-driving cars where trust is often compromised by the opacity of neural network models. The central goal of this study is therefore to address this trust issue. A common approach to imitate human driving behavior through expert demonstrations is imitation learning (IL). However, balancing performance and explainability in these models is a major challenge. To efficiently generate training data
DC fast charging (DCFC) also referred to as L3 charging, is the fastest charging technology to replenish the drivable range of an electric vehicle. DCFC provides the convenience of faster charging time compared to L1 and L2 at the expense of potentially increased battery health degradation. It is known to accelerate battery capacity fade leading to reduced range and lifetime of the EV battery. While there are active efforts and several means to reduce the downsides of DCFC at cell chemistry level, this trade-off is still an important consideration for most battery cells in automotive propulsion applications. Since DCFC is a customer driven technology, informing drivers of the trade-off of each DCFC event can potentially result in better outcomes for the EV battery life. Traditionally, the driver is advised to limit DCFC events without providing quantifiable metrics to inform their decisions during EV charging. A recommendation system for DCFC based on battery health optimization is
Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: Driver anomaly detection and classification: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. Gaze estimation: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. Eye state analysis: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM). Head pose estimation: A CNN-based method that estimates the
Range anxiety is one of the major factors to be dealt with for increasing penetration of EVs in current Automotive market. The major reasons for range anxiety for customers are sparse charging infrastructure availability, limited range of Electric vehicles and range uncertainty due to diverse real-world usage conditions. The uncertainty in real world range can be reduced by increasing the correlation between the testing condition during vehicle development and real-world customer usage condition. This paper illustrates a more accurate test methodology development to derive the real-world range in electric vehicles with experimental validation and system level analysis. A test matrix is developed considering several variables influencing vehicle range like different routes, drive modes, Regeneration levels, customer drive behavior, time of drive, locations, ambient conditions etc. Based on the real-world customer usage inputs, the route type is divided into Core city, City, 2Lane
Wearing Helmet is a critical safety measure not only for riders but also for passengers. However, people often tend to skip wearing these protective headgears, thereby leading to, increased risk of injury or death in the event of an accident. There is a growing necessity to develop innovative methods that automatically monitor and prevent unsafe driving. To address this issue, we have developed a computer vision-based helmet detection system that can detect if a rider has his helmet on in real-time. We use state-of-the-art computer vision-based techniques for helmet detection. This paper covers various aspects of helmet detection, including image pre-processing, feature extraction, and classification. The system is evaluated on performance metrics such as accuracy, precision, and recall. Further enhancement of the system is proposed in the potential directions for future research. The results demonstrate that computer vision-based helmet detection systems hold significant potential to
Good driving practices, encompassing actions like maintaining smooth acceleration, sustaining a consistent speed, and avoiding aggressive maneuvers, can yield several benefits. These practices enhance energy efficiency, reduce accident risks, and significantly lower maintenance costs. Consequently, the presence of a system capable of providing actionable insights to promote such driving behavior is crucial. Addressing this need, the Drive-GPT model is introduced, representing an AI-based generative pre-trained transformer. Within this study, the transformative potential of deep learning networks, specifically based on transformers, is showcased in capturing the typical driving patterns exhibited by individuals in diverse road, traffic, weather, and vehicle health scenarios. The model's training dataset comprises an extensive 90 million data points from multivariate time series originating from telematics systems in 100 vehicles traversing eight distinct Indian cities over a six-month
The commercial vehicle sector (especially trucks) has major role in economic growth of a nation. With improving infrastructure, increasing number of commercial vehicles and growing amount of Vulnerable Road Users (VRUs) on roads, accidents are also increasing. As per RASSI (Road Accident Sampling System India) FY2016-21 database, commercial vehicles are involved in 43% of total accidents on Indian roads. One of the major causes of these accidents is Driver Drowsiness and Inattention (DDI) (approx. 10% contribution in total accidents). This paper describes novel driver-in-loop performance assessment methodology for comprehensive verification of Driver Monitoring System (DMS) for commercial vehicle application. Novelty lies in specification of test subjects, driving styles and variety of road traffic scenarios for verification of DMS system. Test setup is made modular to cater to different platform environments (Heavy, Intermediate, Light) with minor modifications. The test setup
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