  In recent years, imitation learning (IL) has been widely used in industry as
the core of autonomous vehicle (AV) planning modules. However, previous IL
works show sample inefficiency and low generalisation in safety-critical
scenarios, on which they are rarely tested. As a result, IL planners can reach
a performance plateau where adding more training data ceases to improve the
learnt policy. First, our work presents an IL model using the spline
coefficient parameterisation and offline expert queries to enhance safety and
training efficiency. Then, we expose the weakness of the learnt IL policy by
synthetically generating critical scenarios through optimisation of parameters
of the driver's risk field (DRF), a parametric human driving behaviour model
implemented in a multi-agent traffic simulator based on the Lyft Prediction
Dataset. To continuously improve the learnt policy, we retrain the IL model
with augmented data. Thanks to the expressivity and interpretability of the
DRF, the desired driving behaviours can be encoded and aggregated to the
original training data. Our work constitutes a full development cycle that can
efficiently and continuously improve the learnt IL policies in closed-loop.
Finally, we show that our IL planner developed with less training resource
still has superior performance compared to the previous state-of-the-art.
