A Reproducible Benchmark Protocol for Autonomous Micromobility Local Planning in Shared Pedestrian Spaces
Autonomous micromobility vehicles (AMVs) need local planning that balances task progress, safety, and pedestrian interaction quality in pedestrian-rich shared spaces. Evaluating such planners is difficult because studies vary scenarios, seeds, metrics, outputs, and aggregation rules, while single-score leaderboards hide which behaviors produce a ranking. This paper proposes a repeatable, auditable, multi-objective benchmark protocol for AMV local planning. Before comparison, it fixes the scenario set, repeated seeds, measured metrics, stored outputs, planner-interface records, and aggregation procedure. We demonstrate it with a frozen robot_sf_ll7 campaign: 47 shared-space scenarios, three evaluation seeds, and 141 scenario-seed episodes per planner in one differential-drive AMV configuration. The stress test surfaces a descriptive safety–performance separation: a Proximal Policy Optimization (PPO)-family profile reaches higher observed mean task success than a classical reciprocal-avoidance baseline, while that baseline keeps lower collision exposure. Absolute success stays low for both, with most scenarios unsolved by either. Because this learned policy was trained on a superset of the evaluation scenarios, its higher success reflects behavior on the benchmark set, not held-out generalization—an overlap the protocol records per planner rather than hiding in one score. The finding is bounded to these configured pipelines, not a universal planner-family ranking. The contribution is an auditable comparison framework tracing results from the scenario matrix and fixed seeds to episode records, aggregate reports, and manifests.