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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
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 Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Urban traffic scenarios often require a high degree of cooperation between
traffic participants to ensure safety and efficiency. Observing the behavior of
others, humans infer whether or not others are cooperating. This work aims to
extend the capabilities of automated vehicles, enabling them to cooperate
implicitly in heterogeneous environments. Continuous actions allow for
arbitrary trajectories and hence are applicable to a much wider class of
problems than existing cooperative approaches with discrete action spaces.
Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS)
in conjunction with Decoupled-UCT evaluates the action-values of each agent in
a cooperative and decentralized way, respecting the interdependence of actions
among traffic participants. The extension to continuous action spaces is
addressed by incorporating novel MCTS-specific enhancements for efficient
search space exploration. The proposed algorithm is evaluated under different
scenarios, showing that the algorithm is able to achieve effective cooperative
planning and generate solutions egocentric planning fails to identify
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