2,909 research outputs found
An Advanced Coordination Protocol for Safer and more Efficient Lane Change for Connected and Autonomous Vehicles
In this paper we will explore novel ways of utilizing inter-vehicle and vehicle to infrastructure communication technology to achieve a safe and efficient lane change manoeuvre for Connected and Autonomous Vehicles (CAVs). The need for such new protocols is due to the risk that every lane change manoeuvre brings to drivers and passengers lives in addition to its negative impact on congestion level and resulting air pollution, if not performed at the right time and using the appropriate speed. To avoid this risk, we design two new protocols, one is built upon and extends an existing protocol, and it aims to ensure safe and efficient lane change manoeuvre, while the second is an original lane change permission management solution inspired from mutual exclusion concept used in operating systems. This latter complements the former by exclusively granting lane change permissions in a way that avoids any risk of collision. Both protocols are being implemented using computer simulation and the results will be reported in a future work
A Review of Connected and Automated Vehicle Traffic Flow Models for Next-Generation Intelligent Transportation Systems
Connected and Automated Vehicle (CAV) technology is a rapidly developing field that is expected to transform the transportation industry. This study provides an overview of traffic flow models for Connected and Automated Vehicles (CAVs). The study explores the different levels of automation in CAVs and discuss the strengths and limitations of three categories of traffic flow models: microscopic, mesoscopic, and macroscopic. The article highlights that while microscopic models provide a high level of detail and accuracy, they require significant data input and computational resources, making them difficult to scale up to large networks or regions. Mesoscopic models are more computationally efficient but still provide useful detail and can simulate traffic flow over a larger area than microscopic models. Macroscopic models, while most computationally efficient, may not capture the effects of specific traffic management strategies or provide the level of detail necessary to capture individual vehicle movements and driver behaviors. The study emphasizes the need to take into account other factors that can influence CAV traffic flow, such as human-driven vehicles, road infrastructure, and communication protocols. By providing insights into the strengths and weaknesses of each approach, this article aims to facilitate the development of next-generation Intelligent Transportation Systems (ITS) that effectively manage traffic flow and fully realize the potential of CAVs
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
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
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