- New artificial-intelligence based programme enables
autonomous vehicles (AVs) to achieve precise positioning during
adverse weather conditions
- The model showed strong performance across conditions of
rain, fog, and snow, besides day and night
- These results bring AVs one step closer to safe and smooth
all-weather autonomous driving, and ubiquitous adoption.
Researchers at Oxford University’s Department of Computer
Science, in collaboration with colleagues from Bogazici
University, Turkey, have developed a novel artificial
intelligence (AI) system to enable autonomous vehicles (AVs)
achieve safer and more reliable navigation capability, especially
under adverse weather conditions and GPS-denied driving
scenarios. The results have been published today
in Nature Machine Intelligence.
Yasin Almalioglu, who completed the research as part of his DPhil
in the Department of Computer Science, said: ‘The difficulty for
AVs to achieve precise positioning during challenging adverse
weather is a major reason why these have been limited to
relatively small-scale trials up to now. For instance, weather
such as rain or snow may cause an AV to detect itself in the
wrong lane before a turn, or to stop too late at an intersection
because of imprecise positioning.’
To overcome this problem, Almalioglu and his colleagues developed
a novel, self-supervised deep learning model for ego-motion
estimation, a crucial component of an AV’s driving system that
estimates the car's moving position relative to objects observed
from the car itself. The model brought together richly-detailed
information from visual sensors (which can be disrupted by
adverse conditions) with data from weather-immune sources (such
as radar), so that the benefits of each can be used under
different weather conditions.
The model was trained using several publicly available AV
datasets which included data from multiple sensors such as
cameras, lidar, and radar under diverse settings, including
variable light/darkness levels and precipitation. These were used
to generate algorithms to reconstruct scene geometry and
calculate the car’s position from novel data. Under various test
situations, the researchers demonstrated that the model showed
robust all-weather performance, including conditions of rain,
fog, and snow, as well as day and night.
The team anticipate that this work will bring AVs one step closer
to safe and smooth all-weather autonomous driving, and ultimately
a broader use within societies.
Professor Niki Trigoni, from the Department of Computer Science
at Oxford University, who co-supervised the study, said: ‘The
precise positioning capability provides a basis for numerous core
functionalities of AVs such as motion planning, prediction,
situational awareness, and collision avoidance. This study
provides an exciting complementary solution for the AV software
stack to achieve this capability.’
Professor Andrew Markham (Department of Computer Science, Oxford
University), also a co-supervisor for the study, added:
‘Estimating the precise location of AVs is a critical milestone
to achieving reliable autonomous driving under challenging
conditions. This study effectively exploits the complementary
aspects of different sensors to help AVs navigate in difficult
daily scenarios.’
