3,378 research outputs found
DeepSignals: Predicting Intent of Drivers Through Visual Signals
Detecting the intention of drivers is an essential task in self-driving,
necessary to anticipate sudden events like lane changes and stops. Turn signals
and emergency flashers communicate such intentions, providing seconds of
potentially critical reaction time. In this paper, we propose to detect these
signals in video sequences by using a deep neural network that reasons about
both spatial and temporal information. Our experiments on more than a million
frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and
Automation (ICRA), 201
Intent prediction of vulnerable road users for trusted autonomous vehicles
This study investigated how future autonomous vehicles could be further trusted by vulnerable road users (such as pedestrians and cyclists) that they would be interacting with in urban traffic environments. It focused on understanding the behaviours of such road users on a deeper level by predicting their future intentions based solely on vehicle-based sensors and AI techniques. The findings showed that personal/body language attributes of vulnerable road users besides their past motion trajectories and physics attributes in the environment led to more accurate predictions about their intended actions
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
VIENA2: A Driving Anticipation Dataset
Action anticipation is critical in scenarios where one needs to react before
the action is finalized. This is, for instance, the case in automated driving,
where a car needs to, e.g., avoid hitting pedestrians and respect traffic
lights. While solutions have been proposed to tackle subsets of the driving
anticipation tasks, by making use of diverse, task-specific sensors, there is
no single dataset or framework that addresses them all in a consistent manner.
In this paper, we therefore introduce a new, large-scale dataset, called
VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct
action classes. It contains more than 15K full HD, 5s long videos acquired in
various driving conditions, weathers, daytimes and environments, complemented
with a common and realistic set of sensor measurements. This amounts to more
than 2.25M frames, each annotated with an action label, corresponding to 600
samples per action class. We discuss our data acquisition strategy and the
statistics of our dataset, and benchmark state-of-the-art action anticipation
techniques, including a new multi-modal LSTM architecture with an effective
loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201
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