11,071 research outputs found
Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data
By using the onboard sensing and external connectivity technology, connected
and automated vehicles (CAV) could lead to improved energy efficiency, better
routing, and lower traffic congestion. With the rapid development of the
technology and adaptation of CAV, it is more critical to develop the universal
evaluation method and the testing standard which could evaluate the impacts on
energy consumption and environmental pollution of CAV fairly, especially under
the various traffic conditions. In this paper, we proposed a new method and
framework to evaluate the energy efficiency and emission of the vehicle based
on the unsupervised learning methods. Both the real-world driving data of the
evaluated vehicle and the large naturalistic driving dataset are used to
perform the driving primitive analysis and coupling. Then the linear weighted
estimation method could be used to calculate the testing result of the
evaluated vehicle. The results show that this method can successfully identify
the typical driving primitives. The couples of the driving primitives from the
evaluated vehicle and the typical driving primitives from the large real-world
driving dataset coincide with each other very well. This new method could
enhance the standard development of the energy efficiency and emission testing
of CAV and other off-cycle credits
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Many systems for autonomous vehicles' navigation rely on lane detection.
Traditional algorithms usually estimate only the position of the lanes on the
road, but an autonomous control system may also need to know if a lane marking
can be crossed or not, and what portion of space inside the lane is free from
obstacles, to make safer control decisions. On the other hand, free space
detection algorithms only detect navigable areas, without information about
lanes. State-of-the-art algorithms use CNNs for both tasks, with significant
consumption of computing resources. We propose a novel approach that estimates
the free space inside each lane, with a single CNN. Additionally, adding only a
small requirement concerning GPU RAM, we infer the road type, that will be
useful for path planning. To achieve this result, we train a multi-task CNN.
Then, we further elaborate the output of the network, to extract polygons that
can be effectively used in navigation control. Finally, we provide a
computationally efficient implementation, based on ROS, that can be executed in
real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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