2 research outputs found
Time-Dependent Performance Modeling for Platooning Communications at Intersection
With the development of internet of vehicles, platooning strategy has been
widely studied as the potential approach to ensure the safety of autonomous
driving. Vehicles in the form of platoon adopt 802.11p to exchange messages
through vehicle to vehicle (V2V) communications. When multiple platoons arrive
at an intersection, the leader vehicle of each platoon adjusts its movement
characteristics to ensure that it can cross the intersection and thus the
following vehicles have to adjust their movement characteristics accordingly.
In this case, the time-varying connectivity among vehicles leads to the
significant non-stationary performance change in platooning communications,
which may incur safety issues. In this paper, we construct the time-dependent
model to evaluate the platooning communication performance at the intersection
based on the initial movement characteristics. We first consider the movement
behaviors of vehicles at the intersection including turning, accelerating,
decelerating and stopping as well as the periodic change of traffic lights to
construct movement model, and then establish a hearing network to reflect the
time-varying connectivity among vehicles. Afterwards, we adopt the pointwise
stationary fluid flow approximation (PSFFA) to model the non-stationary
behavior of transmission queue. Then, we consider four access categories (ACs)
and continuous backoff freezing of 802.11p to construct the models to describe
the time-dependent access process of 802.11p. Finally, based on the
time-dependent model, the packet transmission delay and packet delivery ratio
are derived. The accuracy of our proposed model is verified by comparing the
simulation results with analytical results.Comment: This paper has been accepted by IEEE Internet of Things Journa
Matching 5G Connected Vehicles to Sensed Vehicles for Safe Cooperative Autonomous Driving
5G connected autonomous vehicles (CAVs) help enhance perception of driving environment and cooperation among vehicles by sharing sensing and driving information, which is a promising technology to avoid accidents and improve road-use efficiency. A key issue for cooperation among CAVs is matching communicating vehicles to those captured in sensors such as cameras, LiDAR, etc.. Incorrect vehicle matching may cause serious accidents. While centimeter level positioning is now available for autonomous vehicles, matching connected vehicles to sensed vehicles (MCSV) is still challenging and has rarely been studied. In this paper, we are motivated to investigate the MCSV problem for 5G CAVs, propose and assess solutions for the problem to bridge the research gap. We formulate the MCSV problem and propose two MCSV approaches to support cooperative driving. The first approach is based on vehicle registration number (VRN), which is unique to identify a vehicle and can be shared among CAVs for MCSV. VRN is hashed before sharing to protect privacy, and will be compared to the shared one for vehicle matching. The second MCSV approach is based on visual features of vehicle’s external views, which are shared with other CAVs and compared to those obtained from visual sensors to match the vehicles of interest. A new MCSV dataset is developed to assess the effectiveness of the proposed approaches. Experiment results show that both approaches are feasible and useful, and they achieve a very low false positive rate, which is critical for cooperative driving safety