Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-learned Intelligence for Integrated Decision and Control of
  Automated Vehicles at Signalized Intersections   Intersection is one of the most complex and accident-prone urban scenarios
for autonomous driving wherein making safe and computationally efficient
decisions is non-trivial. Current research mainly focuses on the simplified
traffic conditions while ignoring the existence of mixed traffic flows, i.e.,
vehicles, cyclists and pedestrians. For urban roads, different participants
leads to a quite dynamic and complex interaction, posing great difficulty to
learn an intelligent policy. This paper develops the dynamic permutation state
representation in the framework of integrated decision and control (IDC) to
handle signalized intersections with mixed traffic flows. Specially, this
representation introduces an encoding function and summation operator to
construct driving states from environmental observation, capable of dealing
with different types and variant number of traffic participants. A constrained
optimal control problem is built wherein the objective involves tracking
performance and the constraints for different participants and signal lights
are designed respectively to assure safety. We solve this problem by offline
optimizing encoding function, value function and policy function, wherein the
reasonable state representation will be given by the encoding function and then
served as the input of policy and value function. An off-policy training is
designed to reuse observations from driving environment and backpropagation
through time is utilized to update the policy function and encoding function
jointly. Verification result shows that the dynamic permutation state
representation can enhance the driving performance of IDC, including comfort,
decision compliance and safety with a large margin. The trained driving policy
can realize efficient and smooth passing in the complex intersection,
guaranteeing driving intelligence and safety simultaneously.

7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex