Nonetheless, different from traditional mobile ad hoc networks [9, 10], VANET is a special mobile ad hoc network for its features, such as the restricted vehicle mobility and highly dynamic.
Optimizing vehicle mobility is of key importance in these cases.
Senior Carrington Motley balances track and field with research on vehicle mobility in sand.
A reinforcement learning algorithm enhances vehicle mobility by finding effective exit strategies in deadlock situations.
These results indicate that SPH may be viable for simulating soft soil in conditions relevant for vehicle mobility considerations.
However, due to the high vehicle mobility, the unreliable vehicular communications such as packet loss and transmission delay can impair the performance of the cooperative driving system (CDS).
However, the unique features of vehicle mobility and the high density of devices in a limited roadway area necessitate further investigation in the automotive domain.
As physics-based methods for modeling soil are developed, it is necessary to validate these simulations against databases relating Cone Index (CI) to vehicle mobility.
This paper presents a novel cluster-based network topology discovery approach for VANET by taking into account both vehicle connectivity and vehicle mobility.
This study focuses on the development of a methodology for the determination of some in situ parameters for off-road vehicle mobility on sandy soils.
Results show that the road wheel system configuration, initial track tension (i.e., the tension in the track system when the vehicle is stationary on a level, hard ground) and track width have significant effects on vehicle mobility over snow.
