  Self-driving vehicles (SDVs) are becoming reality but still suffer from
"long-tail" challenges during natural driving: the SDVs will continually
encounter rare, safety-critical cases that may not be included in the dataset
they were trained. Some safety-assurance planners solve this problem by being
conservative in all possible cases, which may significantly affect driving
mobility. To this end, this work proposes a method to automatically adjust the
conservative level according to each case's "long-tail" rate, named dynamically
conservative planner (DCP). We first define the "long-tail" rate as an SDV's
confidence to pass a driving case. The rate indicates the probability of
safe-critical events and is estimated using the statistics bootstrapped method
with historical data. Then, a reinforcement learning-based planner is designed
to contain candidate policies with different conservative levels. The final
policy is optimized based on the estimated "long-tail" rate. In this way, the
DCP is designed to automatically adjust to be more conservative in
low-confidence "long-tail" cases while keeping efficient otherwise. The DCP is
evaluated in the CARLA simulator using driving cases with "long-tail"
distributed training data. The results show that the DCP can accurately
estimate the "long-tail" rate to identify potential risks. Based on the rate,
the DCP automatically avoids potential collisions in "long-tail" cases using
conservative decisions while not affecting the average velocity in other
typical cases. Thus, the DCP is safer and more efficient than the baselines
with fixed conservative levels, e.g., an always conservative planner. This work
provides a technique to guarantee SDV's performance in unexpected driving cases
without resorting to a global conservative setting, which contributes to
solving the "long-tail" problem practically.
