Abstract
Collective perception is a new paradigm to extend the limited horizon of individual vehicles. Incorporating with the recent vehicle-2-x (V2X) technology, connected and autonomous vehicles (CAVs) can periodically share their sensory information, given that traffic management authorities and other road participants can benefit from these information enormously. Apart from the benefits, employing collective perception could result in a certain level of transmission redundancy, because the same object might fall in the visible region of multiple CAVs, hence wasting the already scarce network resources. In this paper, we analytically study the data redundancy issue in highway scenarios, showing that the redundant transmissions could result in heavy loads on the network under medium to dense traffic. We then propose a probabilistic data selection scheme to suppress redundant transmissions. The scheme allows CAVs adaptively adjust the transmission probability of each tracked objects based on the position, vehicular density and road geometry information. Simulation results confirm that our approach can reduce at most 60% communication overhead in the meanwhile maintain the system reliability at desired levels.
Highlights
Autonomous driving and advanced driving assistant systems (ADAS) in development rely on onboard sensors to build a local dynamic map of a vehicle’s road environment [1]
Our analysis shows that even though highway scenarios do not suffer from blockage effects resulting from buildings, the blockages from other vehicles still have significant impacts on sensors’ field of view, and it is insufficient to overcome this problem by increasing the sensing range
Since our work focuses on investigating the data redundancy issue due to effective field of view (eFoV) overlaps and propose a data selection scheme to reduce generated network traffic, the Matlab program accurately simulate the blockage effects resulting from other vehicles on the road using the computational geometry toolbox, on the other hand, the V2V communications are simplified as the disk model
Summary
Autonomous driving and advanced driving assistant systems (ADAS) in development rely on onboard sensors to build a local dynamic map of a vehicle’s road environment [1]. A CAV should compare its tracked objects with the sensory information received from the network, and only broadcast these who have not been shared for a given interval This method is unreliable because the packet reception rate in vehicular environment suffers from adverse multipath fading channels and collisions due to hidden terminals [8], [9], when the density of vehicles is high and the communication traffic is relatively heavy. It is noticeable that a few recent works have been established to remove data redundancy in floating car data (FCD) collections and disseminations [16]–[18] Their approaches create clusters of vehicles having common features and apply in-network aggregations to reduce communication overhead.
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