66,054 research outputs found
DeepTSP: Deep traffic state prediction model based on large-scale empirical data
Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatio-temporal data to predict the traffic state of large-scale traffic systems. In this study, we first summarize the three challenges faced by large-scale traffic state prediction, i.e., scale, granularity, and sparsity. Based on the domain knowledge of traffic engineering, the propagation of traffic states along the road network is theoretically analyzed, which are elaborated in aspects of the temporal and spatial propagation of traffic state, traffic state experience replay, and multi-source data fusion. A deep learning architecture, termed as Deep Traffic State Prediction (DeepTSP), is therefore proposed to address the current challenges in traffic state prediction. Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states
Multi-Lane Perception Using Feature Fusion Based on GraphSLAM
An extensive, precise and robust recognition and modeling of the environment
is a key factor for next generations of Advanced Driver Assistance Systems and
development of autonomous vehicles. In this paper, a real-time approach for the
perception of multiple lanes on highways is proposed. Lane markings detected by
camera systems and observations of other traffic participants provide the input
data for the algorithm. The information is accumulated and fused using
GraphSLAM and the result constitutes the basis for a multilane clothoid model.
To allow incorporation of additional information sources, input data is
processed in a generic format. Evaluation of the method is performed by
comparing real data, collected with an experimental vehicle on highways, to a
ground truth map. The results show that ego and adjacent lanes are robustly
detected with high quality up to a distance of 120 m. In comparison to serial
lane detection, an increase in the detection range of the ego lane and a
continuous perception of neighboring lanes is achieved. The method can
potentially be utilized for the longitudinal and lateral control of
self-driving vehicles
On the Security of the Automatic Dependent Surveillance-Broadcast Protocol
Automatic dependent surveillance-broadcast (ADS-B) is the communications
protocol currently being rolled out as part of next generation air
transportation systems. As the heart of modern air traffic control, it will
play an essential role in the protection of two billion passengers per year,
besides being crucial to many other interest groups in aviation. The inherent
lack of security measures in the ADS-B protocol has long been a topic in both
the aviation circles and in the academic community. Due to recently published
proof-of-concept attacks, the topic is becoming ever more pressing, especially
with the deadline for mandatory implementation in most airspaces fast
approaching.
This survey first summarizes the attacks and problems that have been reported
in relation to ADS-B security. Thereafter, it surveys both the theoretical and
practical efforts which have been previously conducted concerning these issues,
including possible countermeasures. In addition, the survey seeks to go beyond
the current state of the art and gives a detailed assessment of security
measures which have been developed more generally for related wireless networks
such as sensor networks and vehicular ad hoc networks, including a taxonomy of
all considered approaches.Comment: Survey, 22 Pages, 21 Figure
- …