1,719 research outputs found
Decentralized optimal control in multi-lane merging for connected and automated vehicles
Accepted manuscrip
Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout
Roundabouts in conjunction with other traffic scenarios, e.g., intersections,
merging roadways, speed reduction zones, can induce congestion in a
transportation network due to driver responses to various disturbances.
Research efforts have shown that smoothing traffic flow and eliminating
stop-and-go driving can both improve fuel efficiency of the vehicles and the
throughput of a roundabout. In this paper, we validate an optimal control
framework developed earlier in a multi-lane roundabout scenario using the
University of Delaware's scaled smart city (UDSSC). We first provide conditions
where the solution is optimal. Then, we demonstrate the feasibility of the
solution using experiments at UDSSC, and show that the optimal solution
completely eliminates stop-and-go driving while preserving safety.Comment: 6 Pages, 4 Figures, 1 tabl
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
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
Decentralized Optimal Merging Control for Connected and Automated Vehicles
This paper addresses the optimal control of Connected and Automated Vehicles
(CAVs) arriving from two roads at a merging point where the objective is to
jointly minimize the travel time and energy consumption of each CAV. The
solution guarantees that a speed-dependent safety constraint is always
satisfied, both at the merging point and everywhere within a control zone which
precedes it. We first analyze the case of no active constraints and prove that
under certain conditions the safety constraint remains inactive, thus
significantly simplifying the determination of an explicit decentralized
solution. When these conditions do not apply, an explicit solution is still
obtained that includes intervals over which the safety constraint is active.
Our analysis allows us to study the tradeoff between the two objective function
components (travel time and energy within the control zone). Simulation
examples are included to compare the performance of the optimal controller to a
baseline with human-driven vehicles with results showing improvements in both
metrics.Comment: 16 pages, 2nd version, 20 figure
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