• 한국자동차공학회논문집
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• 제15권1호
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• pp.71-77
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• 2007

Dangerous driving is a major cause of traffic accidents in Korea. It becomes more serious for commercial vehicles due to higher fatality rates. The Safe Driving Management System (SDMS), developed in this research, is a comprehensive solution that monitors and stores driving conditions of vehicles, detects dangerous driving situations, and analyzes the results in real time. The Safe Driving Management System consists of a vehicle movement information controller, a dangerous driving detection algorithm and a vehicle movement data report and analysis program. The dangerous driving detection algorithm detects and classifies dangerous driving conditions into representative cases such as sudden acceleration, sudden braking, sudden lane change, and sudden turning. Both computer simulation and vehicle test have been conducted to develop and verify the algorithm. The Safe Driving Management System has been implemented on commercial buses to verify its reliability and objectivity. It is expected that the system can contribute to prevention of traffic accidents, systemization of safe driving management and reduction of commercial vehicle operation costs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.959-962
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• 1998

In this paper, we propose an efficient vehicle detection and classification algorithm for an electronic toll collection, which is based on shadow robust vehicle presence test. In order to improve the performance of vehicle presence test, we use correlation coefficients between wavelet transformed input and reference images, which takes advanage of textural similarity. We compare the performance of the vehicle presence test with those of some conventional approaches that use variance of frame difference. Experimental results from field test show that the proposed vehicl detection and classification algorithm performs well even under abrupt intensity change due to the characteristics of sensor and occurrence of shadow.

• 대한원격탐사학회지
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• 제24권5호
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• pp.473-481
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• 2008

This paper presents an approach to Back-propagation and Radial Basis Function neural network method with various training set for automatic vehicle detection from aerial images. The initial extraction of candidate object is based on Mean-shift algorithm with symmetric property of a vehicle structure. By fusing the density and the symmetry, the method can remove the ambiguous objects and reduce the cost of processing in the next stage. To extract features from the detected object, we describe the object as a log-polar shape histogram using edge strengths of object and represent the orientation and distance from its center. The spatial histogram is used for calculating the momentum of object and compensating the direction of object. BPNN and RBFNN are applied to verify the object as a vehicle using a variety of non-car training sets. The proposed algorithm shows the results which are according to the training data. By comparing the training sets, advantages and disadvantages of them have been discussed.

• 한국전자파학회:학술대회논문집
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• 한국전자파학회 2002년도 종합학술발표회 논문집 Vol.12 No.1
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• pp.196-199
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• 2002

In this paper, variable threshold estimation algorithm for multiple vehicle detection RADAR is proposed and realized by using DSP for real time processing. The algorithm is developed to get the information of velocity and length of vehicles in multiple lanes by using FMCW RADAR. For real time operation, signal processing part is realized with a high speed DSP board to detect and manipulate the vehicle data and some experimental results are given to show the usefulness of the proposed technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• 한국자동차공학회논문집
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• 제20권3호
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• pp.113-118
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• 2012

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

• 자동차안전학회지
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• 제11권2호
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• pp.11-16
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• 2019

This paper suggests an algorithm for detecting fault of longitudinal controller in autonomous vehicles. Guaranteeing safety in fault situation is essential because electronic devices in vehicle are dependent each other. Several methods like alarm to driver, ceding control to driver, and emergency stop are considered to cope with fault. This research investigates the fault monitoring process in fail-safe system, for controller which is responsible for accelerating and decelerating control in vehicle. Residual is computed using desired acceleration control command and actual acceleration, and detection of its abnormal increase leads to the decision that system has fault. Before computing residual for controller, health monitoring process of acceleration signal is performed using hardware and analytic redundancy. In fault monitoring process for controller, a process model which is fitted using driving data is considered to improve the performance. This algorithm is simulated via MATLAB tool to verify performance.

• 반도체디스플레이기술학회지
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• 제19권3호
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• pp.93-99
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• 2020

A majority of car crash is affected by careless driving that causes extensive economic and social costs, as well as injuries and fatalities. Thus, the research of precise crash detection systems is very significant issues in automotive safety. A lot of crash detection algorithms have been developed, but the coverage of these algorithms has been limited to few scenarios. Road scenes and situations need to be considered in order to expand the scope of a collision detection system to include a variety of collision modes. The proposed algorithm effectively handles the x, y, and z axes of the sensor, while considering time and suggests a method suitable for various real worlds. To reduce nuisance and false crash detection events, the algorithm discriminated between driving mode and parking mode. The performance of the suggested algorithm was evaluated under various scenarios, and it successfully discriminated between driving and parking modes, and it adjusted crash detection events depending on the real scenario. The proposed algorithm is expected to efficiently manage the space and lifespan of the storage device by allowing the vehicle's black box system to store only necessary crash event's videos.

• 제어로봇시스템학회논문지
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• 제14권7호
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• pp.679-688
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• 2008

This paper presents an algorithm capable of detecting leading vehicles using a forward-looking camera. In fact, the accurate measurements of the contact locations of vehicles with road surface are prerequisites for the intelligent vehicle technologies based on a monocular vision. Relying on multistage processing of relevant edge features to the hypothesis generation of a vehicle, the proposed algorithm creates candidate positions being the left and right boundaries of vehicles, and searches for pairs to be vehicle boundaries from the potential positions by evaluating horizontal symmetry. The proposed algorithm is proven to be successful by experiments performed on images acquired by a moving vehicle.