Greenlight Robotics
GreenLight Robotics provides an AI-enabled driver assistance system built specifically for transit buses, trained on bus-specific scenarios to prevent mirror taps, tail swings, and blindspot collisions. The system is tuned to each agency's routes, offering a practical alternative to mitigation spending that operators are more likely to keep using. This helps transit agencies reduce collisions and associated costs.
- Artificial Intelligence
- Automotive Technology
- Hardware
- Internet of Things
- Mobility & Transportation
Funding
Founders
Product
Problem
Transit agencies invest in collision mitigation strategies that operators often disable or ignore, leaving preventable accidents like mirror taps, tail swings, and blindspot strikes to drive up costs and service disruptions. Generic driver assistance systems are not designed for the unique dimensions and turning behaviors of buses, reducing their effectiveness and operator trust.
Solution
GreenLight Robotics offers an AI-enabled driver assistance system purpose-built for transit buses, trained on bus-specific driving scenarios and tuned to each agency's routes. The system proactively warns operators about imminent collision risks, including blindspots and tail swings, allowing them to react before incidents occur. By aligning alerts with real operational conditions, the technology maintains operator engagement and reduces the tendency to tune out warnings. This approach helps agencies direct mitigation budgets toward a solution that measurably lowers collision frequency and related costs.
Target Audience
Primary customers are public transit agencies and municipal bus operators seeking to reduce collision rates, insurance claims, and fleet maintenance costs.
Features
- AI models trained specifically on bus operations, including tail swing and blindspot dynamics not covered by passenger-vehicle systems
- Route-level tuning that customizes alerts to local infrastructure, stops, and driving patterns
- Real-time collision warnings designed to be trusted and retained by operators, reducing alert fatigue
- Focus on high-frequency transit incidents such as mirror taps and side strikes, targeting the most common cost drivers