  Neural network-based driving planners have shown great promises in improving
task performance of autonomous driving. However, it is critical and yet very
challenging to ensure the safety of systems with neural network based
components, especially in dense and highly interactive traffic environments. In
this work, we propose a safety-driven interactive planning framework for neural
network-based lane changing. To prevent over conservative planning, we identify
the driving behavior of surrounding vehicles and assess their aggressiveness,
and then adapt the planned trajectory for the ego vehicle accordingly in an
interactive manner. The ego vehicle can proceed to change lanes if a safe
evasion trajectory exists even in the predicted worst case; otherwise, it can
stay around the current lateral position or return back to the original lane.
We quantitatively demonstrate the effectiveness of our planner design and its
advantage over baseline methods through extensive simulations with diverse and
comprehensive experimental settings, as well as in real-world scenarios
collected by an autonomous vehicle company.

Robotics;Systems and Control;Systems and Control