We further note that in different papers there are subtle

Figure 6. Few-shot learning. The motion planner in Agent-Driver
fine-tuned with 1% data exceeds the state-of-the-art [15] trained on
full data, verifying its few-shot learning ability.
discrepancies in computing these two metrics. For instance,
in UniAD [15] both metrics at k-th second are measured
as the error or collision rate at this certain timestep, while
in ST-P3 [14] and following works [17, 26], these metrics
at k-th second is an average over k seconds. There
are also differences in ground truth objects for collision
calculation in different papers. We explain the details of
metric implementations in appendix for reference. For a
fair comparison, we group different methods based on their
metric implementations (UniAD metric or ST-P3 metric) and
evaluate Agent-Driver on both two metric implementations.
3.2. Comparison with State-of-the-art Methods
ALanguageAgentforAutonomousDriving