About This Dashboard

This dashboard presents a comprehensive analysis of autonomous vehicle safety by comparing accident rates between Waymo and Tesla vehicles. The analysis focuses on safety metrics that are crucial for understanding the current state and future potential of autonomous driving technology.

The visualizations in this dashboard explore:

• Waymo’s operational safety across different locations and contexts
• Tesla’s Autopilot performance compared to manual driving
• Comparative safety metrics between the two leading autonomous vehicle companies
• Temporal trends showing safety improvements over time
• Injury and severity patterns in autonomous vehicle incidents

Each visualization includes detailed observations about what the data reveals and why these insights are important for autonomous vehicle development and public safety policy.

This analysis examines publicly available safety data from both companies to provide an objective comparison of autonomous vehicle safety performance.

Dataset Overview

This analysis combines safety data from two leading autonomous vehicle companies:

Waymo Data:

• Crash reports from various operational cities
• Metrics include crashes, injuries, and airbag deployments per million miles
• Geographic coverage across multiple urban environments
• Comprehensive reporting of incident severity

Tesla Data:

• Quarterly safety reports comparing Autopilot vs manual driving
• Miles per accident metrics over multiple time periods
• Distinction between Autopilot-enabled and manual driving scenarios
• Temporal trends showing safety evolution

Key Variables: Location, Date/Time, Crash Rates, Injury Rates, Autopilot Status, Temporal Patterns, Severity Indicators

Waymo Safety Metrics Heatmap by Location

Observations

What this chart shows: This heatmap visualization displays multiple safety metrics simultaneously across Waymo’s operational locations, allowing for comprehensive comparison of crashes, injuries, airbag deployments, and police reports per million miles.

Why this is interesting:

• Multi-dimensional analysis: Shows relationships between different safety indicators in a single view
• Hotspot identification: Darker areas quickly identify locations and metrics needing attention
• Pattern recognition: Visual patterns reveal correlations between different safety measures

Key insights:

• Most locations show very low injury and airbag deployment rates despite having crashes
• Police reporting patterns vary significantly by location, suggesting different local procedures
• The heatmap format makes it easy to identify both best and worst performing location-metric combinations
• This comprehensive view supports more nuanced safety assessments than single-metric comparisons

Distribution of Waymo Safety Metrics

Observations

What this chart shows: This violin plot displays the statistical distribution of different safety metrics across all Waymo operational locations, showing not just averages but the full range and shape of the data distribution.

Why this is interesting:

• Distribution shape: Violin plots reveal whether data is normally distributed, skewed, or has multiple peaks
• Outlier identification: Shows which locations have unusually high or low safety metrics
• Statistical insight: Quartile lines show median and spread, providing robust statistical measures

Key insights:

• Crash rates show a relatively normal distribution with some outliers
• Injury rates are extremely low with most locations having zero injuries, creating a highly skewed distribution
• Airbag deployment rates are even more concentrated near zero, indicating very few severe crashes
• The visualization clearly shows that while crashes occur, serious safety events are exceptionally rare

Safety Metrics Correlation Matrix

Observations

What this chart shows: This correlation matrix reveals the relationships between different safety metrics, showing how various indicators relate to each other across Waymo’s operational locations.

Why this is interesting:

• Relationship mapping: Identifies which safety metrics tend to occur together or independently
• Predictive insights: Strong correlations can help predict one metric from another
• System understanding: Reveals underlying patterns in autonomous vehicle safety performance

Key insights:

• Strong positive correlations between crashes, injuries, and airbag deployments indicate these metrics move together
• The correlation patterns suggest some locations have generally higher or lower safety profiles across all metrics
• Understanding these relationships helps identify systemic versus isolated safety issues
• The matrix provides a foundation for more sophisticated safety prediction models

Tesla Safety Performance Over Time

Observations

What this chart shows: This faceted timeline visualization shows Tesla’s safety improvement over time, with separate panels for Autopilot and manual driving modes, including trend lines to highlight long-term patterns.

Why this is interesting:

• Trend analysis: Smooth trend lines reveal long-term safety improvement patterns beyond quarterly fluctuations
• Comparative evolution: Separate panels clearly show how both driving modes evolve independently
• Visual clarity: Faceting eliminates overlapping lines while maintaining easy comparison

Key insights:

• Both driving modes show clear upward trends in safety (more miles per accident over time)
• Autopilot consistently maintains higher safety levels and shows steeper improvement
• The trend lines smooth out quarterly variations to reveal underlying improvement patterns
• Visual separation makes it easier to assess the magnitude of differences between modes

Comprehensive Safety Performance Comparison

Observations

What this chart shows: This lollipop chart provides a clean, modern comparison of average safety performance across Waymo and Tesla’s different driving modes, emphasizing the magnitude of differences.

Why this is interesting:

• Clear visual hierarchy: Lollipop design makes it easy to rank performance and see differences
• Magnitude emphasis: The length of each line clearly shows the scale of safety differences
• Clean comparison: Avoids visual clutter while highlighting key performance metrics

Key insights:

• Tesla with Autopilot shows the highest average safety performance
• Both Tesla modes significantly outperform Waymo in this metric
• The visual format makes it immediately clear which systems perform best
• However, operational context differences (highway vs urban, reporting standards) remain important caveats

Waymo Crashes by Season and Year

Observations

What this chart shows: This faceted visualization examines seasonal crash patterns across multiple years, revealing how environmental factors and operational maturity interact to affect autonomous vehicle safety performance.

Why this is interesting:

• Multi-year patterns: Faceting by year reveals whether seasonal effects are consistent or evolving
• Operational maturity: Shows how seasonal performance changes as the system gains experience
• Environmental factors: Identifies which seasons pose the greatest challenges for autonomous vehicles

Key insights:

• Seasonal patterns appear to vary by year, suggesting operational changes or system improvements
• Some years show more pronounced seasonal effects than others
• The faceted design makes it easy to compare seasonal patterns across different years
• This analysis helps identify whether seasonal challenges are being addressed through system improvements

Data Availability: Waymo vs Tesla by Year

Observations

What this chart shows: This visualization shows the temporal distribution and volume of available safety data for both companies, providing important context for understanding the scope and limitations of our comparative analysis.

Why this is interesting:

• Data quality assessment: Understanding data coverage helps evaluate the reliability of conclusions
• Temporal context: Different companies may have different reporting periods or data availability
• Analysis limitations: Gaps or variations in data coverage affect the validity of comparisons

Key insights:

• Data availability varies significantly between companies and across years
• Different reporting schedules and data collection methods affect comparative analysis
• Understanding these limitations is crucial for drawing appropriate conclusions about relative safety performance
• Future analyses should account for these data coverage differences when making safety comparisons

About This Project

Purpose: This dashboard provides a comprehensive analysis of autonomous vehicle safety by comparing crash and injury data from Waymo and Tesla. The goal is to objectively assess the current state of autonomous vehicle safety and identify trends that inform future development and policy decisions.

Data Sources:

• Waymo: Public crash reports and safety data from various operational locations
• Tesla: Quarterly vehicle safety reports comparing Autopilot and manual driving performance

Analysis Focus:

The analysis examines multiple dimensions of autonomous vehicle safety: - Geographic variations in safety performance - Temporal trends and seasonal effects - Injury severity and crash types - Comparative performance between different autonomous driving approaches

Academic Context: This dashboard was created to demonstrate advanced data visualization techniques applied to transportation safety research, with implications for public policy and autonomous vehicle development.

Methodology: The analysis employs diverse visualization techniques including:

• Heatmaps for multi-dimensional geographic safety analysis
• Violin plots for statistical distribution analysis of safety metrics
• Correlation matrices for relationship mapping between safety indicators
• Faceted timelines for temporal trend analysis with trend smoothing
• Lollipop charts for clean performance comparisons
• Seasonal faceting for environmental impact assessment

Key Findings & Implications

Major Findings:

1. Waymo Operational Safety: Shows location-specific variations but consistently low injury and severe crash rates
2. Tesla Autopilot Benefits: Demonstrates consistent safety advantages over manual driving across multiple quarters
3. Continuous Improvement: Both systems show improving safety metrics over time
4. Severity Prevention: Both autonomous systems appear effective at preventing severe crashes

Policy Implications:

• Autonomous vehicles show promise for improving road safety
• Location-specific testing and adaptation remain important
• Continued monitoring and transparent reporting are essential
• Different deployment strategies (full autonomy vs driver assistance) each have merit

Future Research:

• Standardized safety reporting across companies
• Long-term studies of autonomous vehicle safety trends
• Analysis of specific scenarios where autonomous vehicles excel or struggle
• Integration with broader transportation safety research

Limitations:

• Data reporting differences between companies
• Varying operational environments and conditions
• Different definitions of crashes and severity
• Selection effects in autonomous vehicle usage patterns