1,205 research outputs found
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
In depth-sensing applications ranging from home robotics to AR/VR, it will be
common to acquire 3D scans of interior spaces repeatedly at sparse time
intervals (e.g., as part of regular daily use). We propose an algorithm that
analyzes these "rescans" to infer a temporal model of a scene with semantic
instance information. Our algorithm operates inductively by using the temporal
model resulting from past observations to infer an instance segmentation of a
new scan, which is then used to update the temporal model. The model contains
object instance associations across time and thus can be used to track
individual objects, even though there are only sparse observations. During
experiments with a new benchmark for the new task, our algorithm outperforms
alternate approaches based on state-of-the-art networks for semantic instance
segmentation.Comment: IEEE International Conference on Computer Vision 201
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model
In the coming years and decades, autonomous vehicles (AVs) will become
increasingly prevalent, offering new opportunities for safer and more
convenient travel and potentially smarter traffic control methods exploiting
automation and connectivity. Car following is a prime function in autonomous
driving. Car following based on reinforcement learning has received attention
in recent years with the goal of learning and achieving performance levels
comparable to humans. However, most existing RL methods model car following as
a unilateral problem, sensing only the vehicle ahead. Recent literature,
however, Wang and Horn [16] has shown that bilateral car following that
considers the vehicle ahead and the vehicle behind exhibits better system
stability. In this paper we hypothesize that this bilateral car following can
be learned using RL, while learning other goals such as efficiency
maximisation, jerk minimization, and safety rewards leading to a learned model
that outperforms human driving.
We propose and introduce a Deep Reinforcement Learning (DRL) framework for
car following control by integrating bilateral information into both state and
reward function based on the bilateral control model (BCM) for car following
control. Furthermore, we use a decentralized multi-agent reinforcement learning
framework to generate the corresponding control action for each agent. Our
simulation results demonstrate that our learned policy is better than the human
driving policy in terms of (a) inter-vehicle headways, (b) average speed, (c)
jerk, (d) Time to Collision (TTC) and (e) string stability
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