Characterizing driving behavior using automatic visual analysis

In this work, we present the problem of rash driving detection algorithm using a single wide angle camera sensor, particularly useful in the Indian context. To our knowledge this rash driving problem has not been addressed using Image processing techniques (existing works use other sensors such as accelerometer). Car Image processing literature, though rich and mature, does not address the rash driving problem. In this work-in-progress paper, we present the need to address this problem, our approach and our future plans to build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201

Cyber-physical system based on image recognition to improve traffic flow: A case study

Vehicular traffic in metropolitan areas turns congested along either paths or periods. As a case study, we have considered a mass transport system with a bus fleet that rides over exclusive lanes across streets and avenues in an urban area that does not allow the circulation of lightweight vehicles, cargo, and motorcycles. This traffic flow becomes congested due to the absence of restriction policies based on criteria. Moreover, the exclusive lanes are at ground level, decreasing lanes for other vehicles. The main objective of this proposal consists of controlling the access to the exclusive lanes by a cyber-physical system following authorization conditions, verifying the permission status of a vehicle by the accurate recognition of license plates to reduce traffic congestion. Therefore, in the case of invading an exclusive lane without permission, the vehicle owner gets a notification of the fine with the respective evidence

Understanding the problem of bridge and tunnel strikes caused by over-height vehicles

A bridge or tunnel strike is an incident in which a vehicle that is taller than the clearance underneath the structure (over-height), typically a lorry or double-decker bus, collides with the structure causing damage. This can lead to injuries, fatalities and/or, in worst case scenario, train derailments. Bridge and tunnel strikes are costly and expensive. The annual maintenance costs to repair and service the structure have been reported to range in the tens-to-hundreds of thousands (£) while the average cost per strike ranges between £5,000 to £25,000. In this paper, we present a comprehensive synthesis of the nature and scope of the problem of bridge and tunnel strikes, followed by the current state of practice and current state of research. Bridge and tunnel strikes still occur with high frequency, and prevention systems (passive, sacrificial and active) available on the market are often too expensive. Bridge-owners are seeking an affordable yet reliable system that is cheap enough for widespread installation without compromising the accuracy and performance of such a system.This material is based upon work supported by Transport for London.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.trpro.2016.05.48

DSRC-based rear-end collision warning system – An error-component safety distance model and field test

Dedicated short-range communication (DSRC) technology can provide drivers with information about other vehicles that are beyond the normal range of vision and enables the development of driving support systems such as the rear-end collision warning system (ReCWS). However, technology constraints such as communication delays and GPS error affect the accuracy of a DSRC-based ReCWS. This paper proposes a ReCWS design that explicitly represents functional specifications of DSRC technology, including transmission delay specifications that describe the information transmission process and an error-component safety distance specification used to represent the effect of GPS error and the information propagation delay. We propose three collision warning strategies each with different deceleration requirements. The system is assembled with off-the-shelf DSRC and mobile technology that can be readily installed into test vehicles. To test the effectiveness of the proposed ReCWS, we ran a variety of controlled scenarios on a test track. The results show a high degree of warning accuracy. These field test results also provide calibrated system parameter values for future studies and designs of DSRC-based ReCWSs

Monovision-based vehicle detection, distance and relative speed measurement in urban traffic

This study presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an intelligent transportation system. The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection, the authors use a combination of two features: the shadow underneath the vehicle and horizontal edges. They propose a new method for shadow thresholding based on the grey-scale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardised in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions

A Forward Collision Warning System for Smartphones Using Image Processing and V2V Communication

[EN] In this paper, we present a forward collision warning application for smartphones that uses license plate recognition and vehicular communication to generate warnings for notifying the drivers of the vehicle behind and the one ahead, of a probable collision when the vehicle behind does not maintain an established safe distance between itself and the vehicle ahead. The application was tested in both static and mobile scenarios, from which we confirmed the working of our application, even though its performance is affected by the hardware limitations of the smartphones.Patra, S.; Veelaert, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Zamora-Mero, WJ.; Manzoni, P.; Gonzalez, F. (2018). A Forward Collision Warning System for Smartphones Using Image Processing and V2V Communication. Sensors. 18(8):1-17. https://doi.org/10.3390/s18082672S11718

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Intelligent Advisory Speed Limit Dedication in Highway Using VANET

Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle’s travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle’s characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions