13,515 research outputs found
Entrance Capacity of an Automated Highway System
This paper evaluates the entrance capacity and queueing delay for Automated Highway Systems through use of simulations and analytical modeling. Queueing statistics are also used to determine the sustainable capacity of alternative concepts, taking trip length distribution and spacing between ramps into consideration. Based on safety-spacing headways (produced in a separate analysis), the most promising concept utilizes platoons both on the highway and on on-ramps. However, it is unclear whether comparable capacity can be achieved on exit, when vehicles must be decoupled from their platoons, and whether it is safe for vehicles to enter the highway in closely spaced platoons. The analytical evaluation indicates that entrance/exit spacing on the order of one per 2 km or closer would be required to support highways with total capacity on the order of 20,000 vehicles per hour. Most likely, this would be achieved most efficiently if separate dedicated entrances are provided for automated vehicles, to minimize weaving on manual lanes
Advanced Solar Power Systems
The Advanced Solar Power System (ASPS) concentrator uses a technically sophisticated design and extensive tooling to produce very efficient (80 to 90%) and versatile energy supply equipment which is inexpensive to manufacture and requires little maintenance. The advanced optical design has two 10th order, generalized aspheric surfaces in a Cassegrainian configuration which gives outstanding performance and is relatively insensitive to temperature changes and wind loading. Manufacturing tolerances also have been achieved. The key to the ASPS is the direct absorption of concentrated sunlight in the working fluid by radiative transfers in a black body cavity. The basic ASPS design concepts, efficiency, optical system, and tracking and focusing controls are described
Fully automated urban traffic system
The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible
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
Traffic Operations Analysis of Merging Strategies for Vehicles in an Automated Electric Transportation System
Automated Electric Transportation (AET) is a concept of an emerging cooperative transportation system that combines recent advances in vehicle automation and electric power transfer. It is a network of vehicles that control themselves as they traverse from an origin to a destination while being electrically powered in motion – all without the use of connected wires.
AET\u27s realization may provide unparalleled returns in the form of dramatic reductions in traffic-related air pollution, our nation’s dependence on foreign oil, traffic congestion, and roadway inefficiency. More importantly, it may also significantly improve transportation safety by dramatically reducing the number of transportation-related deaths and injuries each year as it directly addresses major current issues such as human error and adverse environmental conditions related to vehicle emissions. In this thesis, a logical strategy in transitioning from today’s current transportation system to a future automated and electric transportation system is identified.
However, the chief purpose of this research is to evaluate the operational parameters where AET will be feasible from a transportation operations perspective. This evaluation was accomplished by performing lane capacity analyses for the mainline, as well as focusing on the merging logic employed at freeway interchange locations. In the past, merging operations have been known to degrade traffic flow due to the interruptions that merging vehicles introduce to the system. However, by analyzing gaps in the mainline traffic flow and coordinating vehicle movements through the use of the logic described in this thesis, mainline traffic operations can remain uninterrupted while still allowing acceptable volumes of merging vehicles to enter the freeway. A release-to-gap merging algorithm was developed and utilized in order to maximize the automated flow of traffic at or directly downstream of a freeway merge point by maximizing ramp flows without causing delay to mainline vehicles. Through these tasks, it is the hope of this research to aid in identifying the requirements and impending impacts of the implementation of this potentially life-altering technology
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