Enhancing a Driver's Situation Awareness using a Global View Map

Cheng, Hong; Liu, Zicheng; Zheng, Nanning

doi:10.1109/icme.2007.4284826

Cited by 7 publications

(6 citation statements)

References 7 publications

(7 reference statements)

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“…[60] propose to use multiple support regions (MSRs) of different sizes surrounding an interest point to choose the best scale of the support region. In [61], the paper proposes a novel method to enhance a driver's situation awareness by dynamically providing a global view of surroundings for the driver. At present, high-precision maps are sorted into two classes, namely ADAS and HAD, respectively.…”

Section: World Modelmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

Self Cite

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With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well.

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Section: World Modelmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

Self Cite

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“…Authors in [10] mount cameras near the side mirrors, to detect vehicles moving in front and on two sides. The work in [11] also suggests a vision-based system to discover surrounding vehicles. It requires the multi-camera system to be mounted on top of the vehicle.…”

Section: Limitations and Future Workmentioning

confidence: 98%

OmniView: A mobile collaborative system for assisting drivers with a map of surrounding traffic

Meng

Nelakuditi

Wang

et al. 2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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To assist drivers and prevent collisions, we propose a system called OmniView that extends driver's vision in all directions, using cameras of multiple collaborating smartphones in the surrounding vehicles. OmniView provides a driver with a traffic map about the relative positions of surrounding vehicles. Under OmniView, each vehicle detects other vehicles in its view, estimates their relative positions, and broadcasts its local map. Upon reception of a map from another vehicle, a vehicle updates its own map by fusing it with the received map. A key issue faced by OmniView is, how does a vehicle address another vehicle in its map? We propose that a vehicle's image itself could be treated as its address. However, if we include images in each map message, the communication overhead will be high. Towards that end, OmniView resolves a vehicle's image to a small unique ID. With this approach, we demonstrate that it is feasible to develop the OmniView system that produces a traffic map in real-time. Besides, through computer vision techniques and the collaboration between vehicles, OmniView could show the positions of surrounding vehicles with reasonable accuracy on the map. Such a traffic map, even without being displayed to the drivers, can act as the common substrate based on which various alerts can be triggered to avoid accidents.

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“…Parallax flow computation was used by Baehring et al for detecting overtaking and close cutting vehicles [11]. For detecting and avoiding Collison, Hong et al had used Radar, LIDAR, camera and omnidirectional camera respectively [12], [16]. They focused on detecting using LIDAR sensor data classifying object as static and dynamic and tracking using extended Kalman filter and for getting a wide view of surrounding situation.…”

Section: Related Workmentioning

confidence: 99%

A robust lane detection and departure warning system

Haloi

Kumar

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this work, we have developed a robust lane detection and departure warning technique. Our system is based on single camera sensor. For lane detection a modified Inverse Perspective Mapping using only a few extrinsic camera parameters and illuminant Invariant techniques is used. Lane markings are represented using a combination of 2nd and 4th order steerable filters, robust to shadowing. Effect of shadowing and extra sun light are removed using Lab color space, and illuminant invariant representation. Lanes are assumed to be cubic curves and fitted using robust RANSAC. This method can reliably detect lanes of the road and its boundary. This method has been experimented in Indian road conditions under different challenging situations and the result obtained were very good. For lane departure angle an optical flow based method were used.

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Enhancing a Driver's Situation Awareness using a Global View Map

Cited by 7 publications

References 7 publications

Survey on Artificial Intelligence for Vehicles

Survey on Artificial Intelligence for Vehicles

OmniView: A mobile collaborative system for assisting drivers with a map of surrounding traffic

A robust lane detection and departure warning system

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