1,429 research outputs found
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Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles
With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag
Control of a lane-drop bottleneck through variable speed limits
In this study, we formulate the VSL control problem for the traffic system in
a zone upstream to a lane-drop bottleneck based on two traffic flow models: the
Lighthill-Whitham-Richards (LWR) model, which is an infinite-dimensional
partial differential equation, and the link queue model, which is a
finite-dimensional ordinary differential equation. In both models, the
discharging flow-rate is determined by a recently developed model of capacity
drop, and the upstream in-flux is regulated by the speed limit in the VSL zone.
Since the link queue model approximates the LWR model and is much simpler, we
first analyze the control problem and develop effective VSL strategies based on
the former. First for an open-loop control system with a constant speed limit,
we prove that a constant speed limit can introduce an uncongested equilibrium
state, in addition to a congested one with capacity drop, but the congested
equilibrium state is always exponentially stable. Then we apply a feedback
proportional-integral (PI) controller to form a closed-loop control system, in
which the congested equilibrium state and, therefore, capacity drop can be
removed by the I-controller. Both analytical and numerical results show that,
with appropriately chosen controller parameters, the closed-loop control system
is stable, effect, and robust. Finally, we show that the VSL strategies based
on I- and PI-controllers are also stable, effective, and robust for the LWR
model. Since the properties of the control system are transferable between the
two models, we establish a dual approach for studying the control problems of
nonlinear traffic flow systems. We also confirm that the VSL strategy is
effective only if capacity drop occurs. The obtained method and insights can be
useful for future studies on other traffic control methods and implementations
of VSL strategies.Comment: 31 pages, 14 figure
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
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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Dynamic Message Sign and Diversion Traffic Optimization
This dissertation proposes a Dynamic Message Signs (DMS) diversion control system based on principles of existing Advanced Traveler Information Systems and Advanced Traffic Management Systems (ATMS). The objective of the proposed system is to alleviate total corridor traffic delay by choosing optimized diversion rate and alternative road signal-timing plan. The DMS displays adaptive messages at predefined time interval for guiding certain number of drivers to alternative roads. Messages to be displayed on the DMS are chosen by an on-line optimization model that minimizes corridor traffic delay. The expected diversion rate is assumed following a distribution. An optimization model that considers three traffic delay components: mainline travel delay, alternative road signal control delay, and the travel time difference between the mainline and alternative roads is constructed. Signal timing parameters of alternative road intersections and DMS message level are the decision variables; speeds, flow rates, and other corridor traffic data from detectors serve as inputs of the model. Traffic simulation software, CORSIM, served as a developmental environment and test bed for evaluating the proposed system. MATLAB optimization toolboxes have been applied to solve the proposed model. A CORSIM Run-Time-Extension (RTE) has been developed to exchange data between CORSIM and the adopted MATLAB optimization algorithms (Genetic Algorithm, Pattern Search in direct search toolbox, and Sequential Quadratic Programming). Among the three candidate algorithms, the Sequential Quadratic Programming showed the fastest execution speed and yielded the smallest total delays for numerical examples. TRANSYT-7F, the most credible traffic signal optimization software has been used as a benchmark to verify the proposed model. The total corridor delays obtained from CORSIM with the SQP solutions show average reductions of 8.97%, 14.09%, and 13.09% for heavy, moderate and light traffic congestion levels respectively when compared with TRANSYT-7F optimization results. The maximum model execution time at each MATLAB call is fewer than two minutes, which implies that the system is capable of real world implementation with a DMS message and signal update interval of two minutes
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