Alternating optimization for multimodal collaborating odometry estimation in CAVs

Cooperative, Connected and Automated Mobility
will enable the close coordination of actions between vehicles,
road users and traffic infrastructures, resulting in profound
socioeconomic impacts. In this context, location and yaw angle
of vehicles is considered vital for safe, secured and efficient
driving. Motivated by this fact, we formulated a multimodal sensor
fusion problem which provides more accurate localization
and yaw information than the original sources. Simultaneously
estimating location and yaw parameters of vehicles can be
treated as the task of cooperative odometry or awareness.
To do so, V2V communication as well as multimodal self
and inter-vehicular measurements from various sensors are
considered for the problem formulation. The solution strategy
is based on the maximum likelihood criterion as well as a novel
alternating gradient descent approach. To simulate realistic
traffic conditions, CARLA autonomous driving simulator has
been used. The detailed evaluation study has shown that each
vehicle, relying only on its neighborhood, is able to accurately
re-estimate both its own and neighboring states (comprised
of locations and yaws), effectively realising the vision of 360◦
awareness.