Market Context — Why This Technology, Why Now

Global efforts to achieve Vision Zero and enhance road safety are accelerating, driven by regulatory pressures and public demand for safer transportation. Simultaneously, the autonomous vehicle market is expanding rapidly, requiring robust solutions for complex, unstructured environments like unsignaled intersections. This technology directly addresses these trends by offering a proven method to reduce accident rates and improve traffic flow, making it a vital component for future mobility ecosystems and smart city initiatives worldwide.

Key Competitive Advantages
01

Significantly reduces accident risk by deriving optimal passing speeds from historical driving data at unknown unsignaled intersections, a challenge for conventional driving assistance systems.

02

Minimizes driver cognitive load by proactively suggesting intersection passing speeds, eliminating the need for complex judgments and reducing fatigue during long-distance driving.

03

Offers high versatility and adaptability to diverse road environments through a unique learning model and RPM parameters, supported by only three prior art documents.

Market Opportunity
Autonomous Vehicle Development
$16.5B globally (AI est.)
As autonomous driving levels advance, addressing complex traffic scenarios is crucial. Ensuring safety at unsignaled intersections is paramount for increasing the social acceptance of autonomous vehicles, driving rapid demand for this technology.
Autonomous vehicle software developers Tier 1 automotive suppliers for ADAS Research divisions of major automakers
Commercial Vehicle Fleet Management
$350M domestically (AI est.)
The logistics industry faces severe driver shortages, making driver assistance technologies essential for reducing burden and accidents. This technology directly enhances overall fleet safety and efficiency, contributing to operational cost reduction and driving adoption.
Large-scale logistics and delivery companies Commercial fleet management software providers Trucking and transportation service providers
Smart City Traffic Infrastructure
$6.5B globally (AI est.)
Achieving optimized traffic flow and zero accidents is a key theme in smart city initiatives. This technology predicts intersection hazards and can integrate with traffic control systems, contributing to overall urban traffic safety and efficiency.
Smart city technology integrators Urban planning and traffic management authorities Infrastructure development companies
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an algorithm for calculating optimal passing speeds at unsignaled intersections based on hazard levels, and a driving assistance device utilizing this algorithm. With only three prior art documents, the technology demonstrates strong novelty and inventiveness, suggesting a robust and difficult-to-invalidate right.

Competitive White Space

Adjacent white space exists in advanced sensor fusion techniques for real-time environmental mapping beyond road conditions, and in predictive analytics for driver behavior in diverse weather conditions, allowing for complementary IP development.

Economic Impact
~$400K/year estimated economic benefit per fleet company (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

By implementing this technology, a fleet company operating 200 vehicles could potentially reduce minor contact accidents (average repair cost ~$3,500/incident (AI est.)) and near-miss delays (estimated ~$200/incident (AI est.)) at unsignaled intersections by an average of 10% annually. This could result in an estimated annual economic benefit of (~$3,500 × 5 incidents + ~$200 × 10 incidents) × 10% × 200 vehicles = ~$400K (AI est.).

Speed to Market
4× faster than in-house development
This technology's core algorithm for optimal driving behavior generation is already established and its specific operational principles are clearly defined in the patent claims. With existing data collection infrastructure for driving behavior and road environment information, rapid system implementation and validation are feasible. As it primarily involves software and algorithm deployment rather than extensive hardware development, significant reductions in development time are expected.
Competitive Positioning

X: Traffic Situation Complexity Handling
Y: Real-time Hazard Avoidance Performance

Business Models & Applications
🚗 Technology Licensing Model
License this technology to autonomous driving system and ADAS developers, allowing vehicle manufacturers and Tier 1 suppliers to integrate it into their products for next-generation mobility solutions with enhanced safety.
🚚 SaaS Solution Provider
Develop and offer a fleet management solution incorporating this technology to logistics and taxi companies. This improves driving safety and efficiency, reducing operational costs and accident rates.
🏙️ Data Analysis & Consulting
Provide data analysis and consulting services to municipalities and traffic infrastructure operators, focusing on unsignaled intersection data collection and analysis. This includes creating traffic hazard maps and optimizing traffic flow based on this technology.
Adjacent Application Opportunities
🎓 Driving Education & Training
Driving Skill Assessment & Training System
This technology could be applied to driving skill assessment systems for new or elderly drivers. By analyzing RPM parameters as individual driving characteristics and identifying behaviors leading to dangerous driving, it has the potential to efficiently support the acquisition of safer driving habits, improving overall road safety outcomes by an estimated 15-20%.
💰 Automotive Insurance
Insurance Premium Optimization Service
RPM parameters derived from actual driving behavior data could be utilized for calculating automobile insurance premiums. This enables the development of tailored insurance products that more accurately reflect individual risk, promoting safe driving and driver awareness. This could lead to a 5-10% reduction in accident claims and improved profitability for insurers.
🤖 Industrial Robotics
Autonomous Navigation for Industrial Vehicles
The technology could be extended to industrial vehicles such as forklifts and AGVs (Automated Guided Vehicles) within factories and warehouses. To reduce collision risks in environments shared with people, it calculates optimal real-time travel speeds that adapt to unknown obstacles and changing conditions, potentially improving operational efficiency by 10% and safety by 20%.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Requirements & Initial Verification
Duration: 3 months
Design the interface between this technology's algorithms and existing vehicle systems. Proceed with data collection and format definition for necessary data (road environment, driving behavior), and conduct initial verification in the target environment.
Phase 2: Algorithm Implementation & Vehicle Testing
Duration: 6 months
Implement the core RPM parameter estimation and optimal driving behavior generation algorithms. After rigorous simulation testing, conduct functional tests and performance evaluations using actual vehicles on a closed course.
Phase 3: System Deployment & Optimization
Duration: 3 months
Confirm system stability and reliability through field tests in real environments, followed by final adjustments. Subsequently, initiate full-scale system deployment to target vehicles, monitoring operations and optimizing performance.
Technical Feasibility
This technology's primary components are algorithms that learn RPM parameters from historical driving behavior data and road environment information to generate optimal speeds. It can be integrated as a software update into existing in-vehicle ECUs and ADAS systems, utilizing data from general-purpose sensors, thus requiring no significant additional hardware investment.
Success Scenario
Upon adopting this technology, a reduction in driver burden and human-error-related accident risks is anticipated. This could lead to improved vehicle utilization rates for logistics companies, potentially enhancing annual operational efficiency by 5%. Furthermore, reduced accident rates may lead to favorable insurance premium costs, allowing companies to transform safety into a competitive advantage.
Patent Record
APPLICATION NO.
特願2015-237801
REGISTRATION NO.
6587529
FILING DATE
2015年12月04日
GRANT DATE
2019年09月20日
EXPIRATION DATE
2035年12月04日
PATENT HOLDER
国立大学法人東京農工大学
Examination History
2018年10月01日
出願審査請求書
2019年08月20日
特許査定