Map Freshness Scorecard

Waymo Open Motion Dataset Analysis

Analysis Methodology:
This analysis identifies potential inconsistencies between static autonomous vehicle maps and real-world driving behavior by detecting trajectory anomalies. The system flags two key indicators of map staleness: Lane Incursions: Vehicle trajectories that deviate more than 2 meters from mapped lane centerlines, suggesting lanes may have been repainted or reconfigured Implied Stop Signs: Locations where vehicles consistently stop despite no mapped stop sign, indicating missing or newly installed traffic control devices The analysis processes trajectories from both ego vehicles (the data-collecting vehicle) and other observed vehicles to increase confidence in detected anomalies. Multiple vehicles exhibiting similar anomalous behavior at the same location strengthens the evidence for map inconsistency. Since the geolocation coordinates of the vehicles were not present in the dataset, the analysis uses normalized cartesian coordinates for the map

Executive Summary

6

Scenarios Analyzed

388

Total Agents

124

Issues Detected

96.9

Average Score

Scenario Analysis Maps

Scenario 1

85.4
Scenario 1 Map
99
Total Agents
106
Issues Found
94
High Confidence
0.8s
Analysis Time

Scenario 2

100.0
Scenario 2 Map
63
Total Agents
0
Issues Found
0
High Confidence
0.8s
Analysis Time

Scenario 3

98.9
Scenario 3 Map
30
Total Agents
3
Issues Found
0
High Confidence
0.8s
Analysis Time

Scenario 4

100.0
Scenario 4 Map
63
Total Agents
0
Issues Found
0
High Confidence
0.8s
Analysis Time

Scenario 5

98.9
Scenario 5 Map
49
Total Agents
4
Issues Found
0
High Confidence
0.8s
Analysis Time

Scenario 6

98.2
Scenario 6 Map
84
Total Agents
11
Issues Found
2
High Confidence
0.8s
Analysis Time

Visual Elements Legend

Trajectory Lines (Vehicle Movement Paths)

Ego Vehicle 1 (Blue - Thick line 8px with markers)
Primary autonomous vehicle being analyzed - highest data quality
Ego Vehicle 2 (Red - Thick line 8px with markers)
Secondary autonomous vehicle (if present)
Other Vehicles (Green - Medium line 3px)
Regular cars, trucks, buses - provides "crowd wisdom"
Non-Vehicle Agents (Orange - Medium line 3px)
Pedestrians, cyclists, scooters - validates infrastructure completeness

Map Infrastructure Elements

Lane Centers
Official map lane centerlines from Waymo dataset
◼
Stop Signs (Red with white border)
Mapped stop sign locations
✕
High Confidence Issues (Red/Orange X's)
Detected map inconsistencies with strong evidence
●
Medium/Low Confidence Issues (Yellow/Orange Circles)
Potential inconsistencies requiring further investigation

Analysis Methodology

Hybrid Approach

Combines high-precision ego vehicle data (LiDAR + cameras) with statistical consensus from crowd behavior to detect map inconsistencies.

Anomaly Detection

Identifies lane incursions (>2m from centerline) and implied stop signs (vehicle stops where no stop sign mapped) using context-aware filtering.

Confidence Levels

Very High (ego + 3+ vehicles), High (ego + 2 vehicles), Medium (3+ crowd), Low (2 crowd) - enables prioritized map updates.

Scoring System

Freshness score (0-100) based on lane accuracy (-5 per issue) and stop sign accuracy (-10 per missing stop) with confidence bonuses.