  In the last few years, robotic technology has been increasingly employed in
agriculture to develop intelligent vehicles that can improve productivity and
competitiveness. Accurate and robust environmental perception is a critical
requirement to address unsolved issues including safe interaction with field
workers and animals, obstacle detection in controlled traffic applications,
crop row guidance, surveying for variable rate applications, and situation
awareness, in general, towards increased process automation. Given the variety
of conditions thatmay be encountered in the field, no single sensor exists that
can guarantee reliable results in every scenario. The development of a
multi-sensory perception systemto increase the ambient awareness of an
agricultural vehicle operating in crop fields is the objective of the Ambient
Awareness for Autonomous Agricultural Vehicles (QUAD-AV) project. Different
onboard sensor technologies, namely stereovision, LIDAR, radar, and
thermography, are considered. Novel methods for their combination are proposed
to automatically detect obstacles and discern traversable from non-traversable
areas. Experimental results, obtained in agricultural contexts, are presented
showing the effectiveness of the proposed methods.
