  As the deployment of autonomous vehicles (AVs) in mixed traffic flow becomes
increasingly prevalent, ensuring safe and smooth interactions between AVs and
human agents is of critical importance. How road users resolve conflicts at
intersections has significant impacts on driving safety and traffic efficiency.
These impacts depend on both the behaviours of AVs and humans' reactions to the
presence of AVs. Therefore, using real-world data to assess and compare the
safety and efficiency measures of AV-involved and AV-free scenarios is crucial.
To this end, this paper presents a high-quality conflict resolution dataset
derived from the open Argoverse-2 motion forecasting data to analyse the safety
and efficiency impacts of AVs. The contribution is twofold: First, we propose
and apply a specific data processing pipeline to select scenarios of interest,
rectify data errors, and enhance the raw data in Argoverse-2. As a result,
5000+ cases where an AV resolves conflict with a human road user and 16000+
conflict resolution cases without AVs are obtained. Motion data is smooth and
consistent in these cases. This open dataset comprises diverse and balanced
conflict resolution regimes. Second, this paper employs surrogate safety
measures and a novel efficiency measure to assess the impact of AVs at
intersections. The results suggest that human drivers exhibit similar safety
and efficiency performances when interacting with AVs and with other human
drivers. In contrast, pedestrians demonstrate more diverse reactions.
Furthermore, due to the safety-prior strategy of AVs, the average efficiency of
AV-involved conflict resolution decreases by 8.6% compared to AV-free cases.
This informative dataset provides a valuable resource for researchers and the
findings give insights into the possible impacts of AVs. The dataset is openly
available via https://github.com/RomainLITUD/conflict_resolution_dataset.
