  Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI)
will contribute to vehicle decision-making in the Intelligent Transportation
System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative
ability to capture mobile targets, is becoming a hot research topic gradually.
Although there are some achievements in the field of MVP in the open space
environment, the urban area brings complicated road structures and restricted
moving spaces as challenges to the resolution of MVP games. We define an
Observation-constrained MVP (OMVP) problem in this paper and propose a
Transformer-based Time and Team Reinforcement Learning scheme ($ \text{T}^3
$OMVP) to address the problem. First, a new multi-vehicle pursuit model is
constructed based on decentralized partially observed Markov decision processes
(Dec-POMDP) to instantiate this problem. Second, by introducing and modifying
the transformer-based observation sequence, QMIX is redefined to adapt to the
complicated road structure, restricted moving spaces and constrained
observations, so as to control vehicles to pursue the target combining the
vehicle's observations. Third, a multi-intersection urban environment is built
to verify the proposed scheme. Extensive experimental results demonstrate that
the proposed $ \text{T}^3 $OMVP scheme achieves significant improvements
relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is
available at https://github.com/pipihaiziguai/T3OMVP.
