Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

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Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras.

Since the traffic data has a high volume, high diversity and high speed of production, the traditional systems cannot process them accurately In this paper, a big data based system was designed and implemented for identifying the offenders’ behaviors The proposed Traffic Violation Detection System (TVD system) included four main phases which were described using the MAPE methodology In the monitor phase, unstructured data, such as videos captured by traffic control cameras as well as the images and the descriptions provided by traffic officers, were collected In the analysis phase, the knowledge base of unsafe driving behaviors was created and classified In this phase, a standard Work Breakdown Structure for unsafe behaviors was created by the experts in traffic control In the Plan phase, in order to detect unsafe driving behaviors related to police descriptions for collected images, and to detect unsafe behaviors from video cameras, Behavior-Based Safety process with Map/ Reduce technique and Vector Space Model (VSM) were employed In the last phase, all types of data, including the structured data and the multimedia/unstructured data, along with the types and details of violations, were stored on the Hadoop Distributed File System The prototype of the proposed TVD system was successfully implemented for a few common violations The results showed that by applying big data technologies, the driving violations could be detected more accurately using the combination of the structured and unstructured data The results indicate that compared to the sequential program, Hadoop only with a single slave-node decreases the processing time of big data by more than 70% Also, by increasing the number of slave nodes from 1 to 7 in the police descriptions and images of surveillance cameras, the processing time reduces by 6087% and 70%, respectively Thus, the TVD performance increases by more than 75% as the number of data nodes boosts Based on the results, it can be concluded that by identifying unsafe driving behaviors, it is possible to diminish traffic accidents and the damage caused by them at a satisfactory level Also, the authors decided to compare these studies in terms of qualitative criteria, such as fieldwork and behavioral identification, and quantitative criteria

Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

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Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach.

The autonomous vehicle can recognize and understand environment, self-control, and achieve the driving level of the human driver. To create this kind of system, the following work was carried out: ...

https://journals.sagepub.com/doi/pdf/10.1177/1687814020956494

Heungsuk Kim

Papers

A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on Highway