Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network based Interactive Lane Changing Planner in Dense Traffic
  with Safety Guarantee   Neural network based planners have shown great promises in improving
performance and task success rate in autonomous driving. However, it is very
challenging to ensure safety of the system with learning enabled components,
especially in dense and highly interactive traffic environments. In this work,
we propose a neural network based lane changing planner framework that can
ensure safety while sustaining system efficiency. To prevent too conservative
planning, we assess the aggressiveness and identify the driving behavior of
surrounding vehicles, then adapt the planned trajectory for the ego vehicle
accordingly. The ego vehicle can proceed to change lanes if a safe evasion
trajectory exists even in the worst case, otherwise, it can hesitate around
current lateral position or return back to the original lane. We also
quantitatively demonstrate the effectiveness of our planner design and its
advantage over other baselines through extensive simulations with diverse and
comprehensive experimental settings.

24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th