Real-Time Forward Collision Warning System Using Nested Kalman Filter for Monocular Camera

Lim, Qun; He, Yichang; Tan, U-Xuan

doi:10.1109/robio.2018.8665220

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…The raw depth image has holes whereby the pixel values are noisy and not reliable. To reduce noise and inaccuracy, filters are added to improve the performance of the sensor [41]. An adaptive directional filter [42] is applied to the depth image to fill up holes, suppressing the depth images.…”

Section: B Methodology Of Dynamic Pavement and Panthera Synergymentioning

confidence: 99%

Reconfiguration During Locomotion by Pavement Sweeping Robot With Feedback Control From Vision System

Hayat

et al. 2020

IEEE Access

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Routine cleaning the pavement is an essential requirement to maintain a sustainable environment for social life. The different width and type of pavements raise the challenges for autonomous vehicles with fixed shape to operate effectively. In this paper, we introduce the vision based reconfiguration of self-reconfigurable pavement sweeping robot called Panthera, which can adjust its frame width to ease the cleaning tasks to become friendly with different pavement geometry. The expansion and compression operations of the Panthera width are implemented by rotating one high torque motor connecting with the lead screw rod to change the opening angle of linkage hinges. The Panthera cleaning and locomotion operations are synchronized with changing the robot width according to the output of detected pavement width. To this end, the segmented pavement leveraged on the masked based deep convolutional neural network (DCNN) is used as input for the proposed closed-loop feedback control method, enabling the robot to adjust the requirement of changing the width during locomotion accurately. The proposed PID scheme takes into account the robot kinematic design with the flexibility of width changing modes. The experiments were carried out in real environments demonstrated the autonomous reconfiguration robot width with various locomotion scenarios on pavements of varying width.

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Section: B Methodology Of Dynamic Pavement and Panthera Synergymentioning

confidence: 99%

Reconfiguration During Locomotion by Pavement Sweeping Robot With Feedback Control From Vision System

Hayat

et al. 2020

IEEE Access

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“…This means that the distance is calculated beforehand, and the LUT is not easily transferable as it is based on the position of the camera on the car. In the papers by Lim et al (2018) and Salari and Ouyang (2013), it uses the width of the vehicle to obtain the distance and the time-to-collision data. However, the paper by Salari and Ouyang (2013), does not show how it filters the data as using this method is very noisy, and the article by Lim et al (2018) shows a nested Kalman filter for displacement and velocity individually, but both of them can be combined into a single Kalman filter which requires less computational power.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the papers by Lim et al (2018) and Salari and Ouyang (2013), it uses the width of the vehicle to obtain the distance and the time-to-collision data. However, the paper by Salari and Ouyang (2013), does not show how it filters the data as using this method is very noisy, and the article by Lim et al (2018) shows a nested Kalman filter for displacement and velocity individually, but both of them can be combined into a single Kalman filter which requires less computational power. Similar to the paper by Lim et al (2018), this paper will use computer vision and obtain distance based on width, but it uses the nested Kalman filter in a different way where it first uses a Kalman filter on relative distance and speed and the subsequent filter on time-to-collision based on a constant velocity model.…”

Section: Limitationmentioning

confidence: 99%

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Lim

Muhammad

et al. 2021

JICV

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Purpose The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

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“…Qun Lim et al [7] proposed a method called Forward Collision Warning System, to overcome the pre-existing vehicle detection system that uses Support Vector Machine. SVM method is unreliable in terms of speed and real time applications.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Rider Assistance System Using Machine L earning for Two Wheel Vehicles

John*,

R L

2019

IJEAT

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As far as a rider is concerned, safety is the first priority while driving a vehicle. There have been significant changes over the years in the design of four wheeler in terms of security. During the design of vehicles manufacturers are incorporating many modifications to the existing model so that the security is more. But when the safety of motorbikes is concerned this is not true, as there exits some blind spots which are not visible to the rider through the mirrors. Blind spots are the rare quarter areas towards the rear of the vehicle on both sides, vehicles in the contiguous paths of the road may fall into these vulnerable sides and driver may not be able to see them using only the mirrors. In this paper we have introduced a new approach using machine learning tools to detect the obstacles and to alert rider and there by reducing the risk of collision. This system could reduce significant amount of risk that the rider is going to face because of the obstacles which are not visible through the mirrors.

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Real-Time Forward Collision Warning System Using Nested Kalman Filter for Monocular Camera

Cited by 14 publications

References 13 publications

Reconfiguration During Locomotion by Pavement Sweeping Robot With Feedback Control From Vision System

Reconfiguration During Locomotion by Pavement Sweeping Robot With Feedback Control From Vision System

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

An Intelligent Rider Assistance System Using Machine L earning for Two Wheel Vehicles

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