39 research outputs found
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
This work tackles scene understanding for outdoor robotic navigation, solely
relying on images captured by an on-board camera. Conventional visual scene
understanding interprets the environment based on specific descriptive
categories. However, such a representation is not directly interpretable for
decision-making and constrains robot operation to a specific domain. Thus, we
propose to segment egocentric images directly in terms of how a robot can
navigate in them, and tailor the learning problem to an autonomous navigation
task. Building around an image segmentation network, we present a generic
affordance consisting of 3 driveability levels which can broadly apply to both
urban and off-road scenes. By encoding these levels with soft ordinal labels,
we incorporate inter-class distances during learning which improves
segmentation compared to standard "hard" one-hot labelling. In addition, we
propose a navigation-oriented pixel-wise loss weighting method which assigns
higher importance to safety-critical areas. We evaluate our approach on
large-scale public image segmentation datasets ranging from sunny city streets
to snowy forest trails. In a cross-dataset generalization experiment, we show
that our affordance learning scheme can be applied across a diverse mix of
datasets and improves driveability estimation in unseen environments compared
to general-purpose, single-dataset segmentation.Comment: Accepted in Robotics and Automation Letters (RA-L 2022).
Supplementary video available at https://youtu.be/q_XfjUDO39
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios
This paper discusses ongoing work in demonstrating research in mobile
autonomy in challenging driving scenarios. In our approach, we address
fundamental technical issues to overcome critical barriers to assurance and
regulation for large-scale deployments of autonomous systems. To this end, we
present how we build robots that (1) can robustly sense and interpret their
environment using traditional as well as unconventional sensors; (2) can assess
their own capabilities; and (3), vitally in the purpose of assurance and trust,
can provide causal explanations of their interpretations and assessments. As it
is essential that robots are safe and trusted, we design, develop, and
demonstrate fundamental technologies in real-world applications to overcome
critical barriers which impede the current deployment of robots in economically
and socially important areas. Finally, we describe ongoing work in the
collection of an unusual, rare, and highly valuable dataset.Comment: accepted for publication at the IEEE Intelligent Vehicles Symposium
(IV), Workshop on Ensuring and Validating Safety for Automated Vehicles
(EVSAV), 2020, project URL:
https://ori.ox.ac.uk/projects/sense-assess-explain-sa
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
Reliable outdoor deployment of mobile robots requires the robust
identification of permissible driving routes in a given environment. The
performance of LiDAR and vision-based perception systems deteriorates
significantly if certain environmental factors are present e.g. rain, fog,
darkness. Perception systems based on FMCW scanning radar maintain full
performance regardless of environmental conditions and with a longer range than
alternative sensors. Learning to segment a radar scan based on driveability in
a fully supervised manner is not feasible as labelling each radar scan on a
bin-by-bin basis is both difficult and time-consuming to do by hand. We
therefore weakly supervise the training of the radar-based classifier through
an audio-based classifier that is able to predict the terrain type underneath
the robot. By combining odometry, GPS and the terrain labels from the audio
classifier, we are able to construct a terrain labelled trajectory of the robot
in the environment which is then used to label the radar scans. Using a
curriculum learning procedure, we then train a radar segmentation network to
generalise beyond the initial labelling and to detect all permissible driving
routes in the environment.Comment: accepted for publication at the IEEE Intelligent Transportation
Systems Conference (ITSC) 202
Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems
International audienceWith the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this paper, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Lastly, we propose a privacy enhancement algorithm that mitigates the leakage of this private information
Implicit Perception Simplicity and Explicit Perception Complexity in Sensorimotor Comunication
[No abstract available