ECE-VDTDA: A robust and computationally efficient collision avoidance system for driver assistance in foggy weather.

Vehicle detection and tracking have been promising application in traffic surveillance and vehicular network.However, the vision-based approach still remains a challenging task due to the problems of illumination variation, shadow and occlusion.In this paper, we propose a robust framework mainly concatenates on two aspects: adaptive vehicle detection with shadow removal, and vehicle tracking with occlusion handling.Firstly, for vehicle detection stage, an improved ViBe algorithm with ghost suppression is adopted to extract moving vehicle region.Then moving shadow is removed by integrating improved color with texture feature.Then, aiming to achieve the multi-vehicle tracking, we propose an enhanced histogram of the oriented gradient combined with HSV color space based on particle filter (ECHOGPF).Finally, we employ the occlusion detection and occlusion segmentation to refine our system, which are based on one-dimensional maximum entropy and the least square ellipse fitting.Experiments on popular datasets show that our proposed system has a good effectiveness, e.g., the accuracy is 95% on the vehicle tracking.

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user’s defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

<div class="section abstract"><div class="htmlview paragraph">New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework’s elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS’ components, and different types of input attributes to these MLA are proposed for some basic ADAS’ components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-Learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS’ components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.</div></div>

PurposeAdvanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.Design/methodology/approachA random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.FindingsThe acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.Originality/valueFew research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

In this research paper, our approach is to present a practical solution for all the road car accidents which takes place. Our model will monitor the preceding vehicles and simultaneously it will calculate the distance between the vehicles. Now, if the preceding vehicle gets too close, then our model willwarnthat driver of an impending crash. The driver of the vehicle will simply be alerted by any audible signal after reaching the threshold distance between the vehicles. This model will be very much helpful, especially for the learners. This calculation of the distance between the vehicles will be done by our CNN based model and a camera which will be located on the windshield to click the rear side images of the preceding vehicle in real time. In our experimental results, we can see that our model can calculate the distance and we would be able to achieve an overall good accuracy. In the past, researchers used these technologies like ADAS and FCW system which are entirely dependent on the hardware and cost-effective as well. So, we are using CNN in FCW system instead of using the hardware like radars. In this paper, the concept of Advanced Driver Assistance System (ADAS) and Forward Collision Warning (FCW) are also described.

This study demonstrates the need for a holistic safety‐impact assessment of an advanced driver assistance system (ADAS) and its effect on eye‐glance behaviour. It implements a substantial incremental development of the what‐if (counterfactual) simulation methodology, applied to rear‐end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off‐road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off‐road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off‐road glances together with the safety effects provided by the ADAS.

[This corrects the article DOI: 10.1371/journal.pone.0189145.].

Vision-based automatic detection and tracking of on-road vehicles is a demanding research area in intelligent transportation systems (ITS). Several investigation reports are available in the literature in this research field using various machine learning (ML) techniques. This article proposes a method for on-road vehicle detection and tracking in varying weather conditions by modifying the structure of you only look once (YOLO), a recently developed deep learning model in computer vision for detecting objects. ML-based systems repurpose classifiers to detect the vehicles, whereas in YOLO model, a single convolutional neural network (CNN) predicts the detecting bounding boxes and the class probabilities for those boxes together in one evaluation from the full images. As the entire detection is performed using a single network, the detection process can be viewed as an end-to-end system. In this work, 16 convolutional layers of YOLO have been used along with two fully connected layers at the end. The vehicles have been detected in varying weather conditions. Two different public data sets, namely CDNet 2014 and LISA 2010 data sets have been used to evaluate the system performance, and it outperforms state-of-the-art results.

The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.

Front Collision Warning(FCW) is a critical safety function of Advanced Driver Assistance System(ADAS). Recently, many researches related to FCW systems which use monocular camera image processing have been introduced. In this paper, we propose an FCW system for highway environment based on vehicle detection using Convolutional Neural Network(CNN) as a classifier. Adaptive Region-of-Interest(ROI) is set using lane detection to enhance speed and detection performance of the system. We measure the distance between our vehicle and the detected vehicle in front by calculating the ratio between the lane width of the position of the detected vehicle and our vehicle, respectively. Time-to-Collision(TTC) is used as a collision warning index. For FHD(1920×1080) black-box camera images taken in highway environment, the detection rate of the proposed CNN is 99.1%, and the execution time of the system is 19.8ms per frame.

With the potential to increase road safety and provide economic benefits, intelligent vehicles have elicited a significant amount of interest from both academics and industry. A robust and reliable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment. The millimeter-wave radar and the monocular camera are two vehicular sensors commonly used for vehicle detection and tracking. Despite their advantages, the drawbacks of these two sensors make them insufficient when used separately. Thus, the fusion of these two sensors is considered as an efficient way to address the challenge. This paper presents a collaborative fusion approach to achieve the optimal balance between vehicle detection accuracy and computational efficiency. The proposed vehicle detection and tracking design is extensively evaluated with a real-world data set collected by the developed intelligent vehicle. Experimental results show that the proposed system can detect on-road vehicles with 92.36% detection rate and 0% false alarm rate, and it only takes ten frames (0.16 s) for the detection and tracking of each vehicle. This system is installed on Kuafu-II intelligent vehicle for the fourth and fifth autonomous vehicle competitions, which is called “Intelligent Vehicle Future Challenge” in China.

Practice makes better – Learning effects of driving with a multi-stage collision warning

Vision-based vehicle tracking on highway traffic using bounding-box features to extract statistical information

<div class="section abstract"><div class="htmlview paragraph">As a result of the development of Event Data Recorders (EDR) and the recent FMVSS regulation 49 CFR 563, today’s automobiles provide a limited subset of electronic data measurements of a vehicle’s state before and during a crash. Prior to this data, the only information available about the vehicle movements before or during a collision had come from physical evidence (e.g. tire marks), witnesses, aftermarket camera systems on vehicles, and ground-based cameras that were monitoring vehicle traffic or used for security surveillance. Today’s vehicles equipped with Advanced Driver Assistance Systems (ADAS) have vehicle-based sensors that measure information about the environment around a vehicle including other vehicles, pedestrians, and fixed wayside objects. Vehicles equipped with these ADAS systems use primarily radar, lidar, ultrasonic, and/or image sensors either in standalone operation or in combination to establish the range and movement of potential hazardous objects (e.g. other vehicles, poles, and pedestrians) around a vehicle. The data from these sensors is unique because it measures objective environment information surrounding a vehicle, which can play a role in reconstructing and understanding the contributing factors of an accident. As more vehicles become equipped with advanced safety systems and the requisite sensors for automated vehicles, accident investigators will need to become familiar with system functionality, the data that the sensors measure and may record, as well as the limitations of that data in order to effectively and accurately analyze a crash.</div><div class="htmlview paragraph">This paper reviews the sensors in ADAS collision avoidance systems that may be present on current vehicles and how the data from a vehicle based radar may be used to reconstruct a collision. A radar sensor mounted on a host vehicle is experimentally evaluated using stationary objects, and a stationary and moving radar. The results demonstrate how an investigator would use the radar data to recognize a fixed object in the environment and analyze the relative position with respect to the host vehicle. The analysis of this radar data includes the limitations, accuracy, and validation of the particular radar sensor data. In addition, the authors propose in general how data from radars could be used to elucidate the location of fixed objects in the environment surrounding a vehicle for the purpose of understanding an accident.</div></div>

Aiming at vehicle detection and tracking problems in video monitoring and controlling system, this paper mainly studies vehicle detection and tracking problems in conditions of high traffic density in daytime. This paper is distinguished by two key contributions. First, we develop an improvement — SEAP(Simple but Efficient After Process) which checks the detection results in an accurate way and is an after process of Adaboost [1] detector which used to detect car in every frame. Second, we propose a tracking algorithm named 4-states tracking algorithm based on Kalman[5] linear filter. Tracking results turn unsteady as traffic density grows higher because of much more false positives and false negatives appear. However, 4-states tracking algorithm can solve this problem in an easy way by introducing FSM (Finite State Machine) into tracking algorithm. Finally, we implement a real-time vehicle detection and tracking system with the upper methods. Experiments give good results in relative crowded Conditions.