113,232 research outputs found
PICES Press, Vol. 11, No. 2, July 2003
Cover [pdf, 1.2 Mb]
PICES Science Board and Governing Council hold their first joint meeting [pp. 1-3] [pdf, 0.2 Mb]
3rd International Zooplankton Production Symposium [pp. 4-7] [pdf, 0.6 Mb]
The state of the eastern North Pacific entering spring 2003 [pp. 8-9] [pdf, 0.4 Mb]
The state of the western North Pacific in 2002 [pp. 10-13] [pdf, 0.6 Mb]
The Bering Sea: Current status and recent events [pp. 14-15] [pdf. 0.7 Mb]
Patricia Livingston [pp. 16-19] [pdf. 0.5 Mb]
Recent changes in the abundance of northern anchovy (Engraulis mordax) off the Pacific Northwest, tracking a regime shift? [pp. 20-21] [pdf. 0.6 Mb]
Developing new scientific programs in PICES [pp. 22-26] [pdf. 0.2 Mb]
Report of the Yokohama 2003 MODEL Task Team Workshop to develop a marine ecosystem model of the North Pacific Ocean including pelagic fishes [pp. 27-29] [pdf. 0.5 Mb]
3rd PICES Workshop on the Okhotsk Sea and adjacent Areas [pp.30-31] [pdf. 0.4 Mb]
Recent oceanographic and marine environmental studies at FERHRI [pp.32-34] [pdf. 0.4 Mb]
Symposium Announcement [p. 35] [pdf. 0.3 Mb]
PICES announcements [p. 36] [pdf. 0.3 Mb
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
- …