  A critical requirement for automated driving systems is enabling situational
awareness in dynamically changing environments. To that end vehicles will be
equipped with diverse sensors, e.g., LIDAR, cameras, mmWave radar, etc.
Unfortunately the sensing `coverage' is limited by environmental obstructions,
e.g., other vehicles, buildings, people, objects etc. A possible solution is to
adopt collaborative sensing amongst vehicles possibly assisted by
infrastructure. This paper introduces new models and performance analysis for
vehicular collaborative sensing and networking. In particular, coverage gains
are quantified, as are their dependence on the penetration of vehicles
participating in collaborative sensing. We also evaluate the associated
communication loads in terms of the Vehicle-to-Vehicle (V2V) and
Vehicle-to-Infrastructure (V2I) capacity requirements and how these depend on
penetration. We further explore how collaboration with sensing capable
infrastructure improves sensing performance, as well as the benefits in
utilizing spatio-temporal dynamics, e.g., collaborating with vehicles moving in
the opposite direction. Collaborative sensing is shown to greatly improve
sensing performance, e.g., improves coverage from 20% to 80% with a 20%
penetration. In scenarios with limited penetration and high coverage
requirements, infrastructure can be used to both help sense the environment and
relay data. Once penetration is high enough, sensing vehicles provide good
coverage and data traffic can be effectively `offloaded' to V2V connectivity,
making V2I resources available to support other in-car services.
