Market Context — Why This Technology, Why Now

Global regulatory bodies are imposing stricter safety standards for autonomous and assisted driving systems, making robust collision avoidance a paramount concern for OEMs and fleet operators. Public trust in self-driving technology hinges on its proven ability to prevent accidents, especially in complex urban environments. Furthermore, the economic burden of vehicle accidents, including insurance costs and downtime, drives demand for solutions that can demonstrably reduce incident rates by a significant margin.

Key Competitive Advantages
01

Generates the safest route rapidly based on driving data, reducing accident risk compared to conventional reactive systems.

02

Enhances safety by learning from non-collision data, enabling more robust hazard avoidance in unpredictable situations.

03

Maximizes safety with other road users by selecting the safest path from multiple options, ensuring harmony with the surrounding environment.

Market Opportunity
Autonomous Driving / ADAS Market
$65B–$70B globally (AI est.)
As autonomous driving levels advance, highly sophisticated hazard prediction and avoidance technologies become essential. Reducing traffic accidents is a societal imperative.
Autonomous vehicle developers Tier 1 ADAS suppliers Ride-sharing technology companies
Logistics / Fleet Management Market
$10B–$10B globally (AI est.)
Driver shortages and an aging workforce necessitate efficient and safe fleet operations. Reducing accident risk directly translates to significant cost savings.
Large logistics and shipping companies Commercial fleet management software providers Last-mile delivery service providers
Smart City Traffic Infrastructure
$3B–$3.5B globally (AI est.)
Demand is growing for advanced driving assistance systems that integrate with infrastructure, not just individual vehicles, to optimize urban traffic flow and prevent accidents across entire cities.
Smart city solution providers Traffic management system integrators Urban planning and development firms
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a core algorithm for driving assistance, specifically focusing on generating safe paths by leveraging driving behavior data and an inverse risk probability model. Its patentability was affirmed against numerous existing technologies, indicating a stable and robust right with low invalidation risk, offering strong defensive capabilities for implementing companies.

Competitive White Space

This patent focuses on the core algorithm for safe path generation. A licensee could develop complementary IP in advanced sensor fusion techniques or novel human-machine interface (HMI) designs for driver interaction with the system.

Economic Impact
~$1M–$5M/year estimated accident loss risk reduction per large-scale operation (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming a fleet of 1,000 vehicles, if the average annual accident rate is reduced from 0.5% to 0.05% (a 10x reduction) using this technology, and the average loss per accident is $35K (AI est.), the annual savings would be (1,000 vehicles × 0.5% × $35K) - (1,000 vehicles × 0.05% × $35K) = $150K (AI est.). This scales proportionally with the number of vehicles, projecting a multi-million dollar economic impact for large-scale operations.

Speed to Market
4× faster than in-house development
This technology features established inverse risk probability models and path generation algorithms utilizing driving behavior data and high-definition map information. This significantly reduces development time compared to starting in-house R&D from scratch. It is designed for integration with existing in-vehicle sensors and communication infrastructure, minimizing the need for new hardware development. Integrating these validated algorithms into a licensee's system could shorten time-to-market by approximately 2.5 years.
Competitive Positioning

X: Comprehensiveness of Safety Evaluation
Y: Real-time Path Generation Capability

Business Models & Applications
🚗 Licensing to Autonomous Driving Systems
Offer algorithm licenses for this technology to companies developing autonomous driving systems. This could enable higher levels of safety and reliability, contributing to significant product differentiation.
🚚 Fleet Management & Operational Safety Solutions
Provide accident prediction and avoidance solutions integrated with fleet management systems for logistics and transportation companies. This could enhance operational safety, reduce insurance premiums, and contribute to overall cost reduction.
💡 Provision as an ADAS Function Module
Offer this as a high-precision driving assistance function module to automotive manufacturers and Tier 1 suppliers. It has the potential to become a core technology for next-generation ADAS (Advanced Driver-Assistance Systems).
Adjacent Application Opportunities
🚚 Logistics & Delivery Robots
Precision Hazard Avoidance for Robotics
Apply this technology to delivery robots in warehouses or on public roads to generate optimal paths while avoiding collisions with pedestrians and obstacles. This could enable safe and efficient autonomous navigation even in confined or complex environments, reducing incidents by an estimated 80%.
🚜 Construction Machinery & Heavy Equipment
Safe Operation of Heavy Equipment in Hazardous Zones
Apply this technology to heavy equipment like excavators and bulldozers on construction sites. It could prevent contact accidents with workers and avoid terrain hazards, supporting safe automated or semi-automated operations and potentially reducing site accidents by 60%.
🚁 Drones & UAVs
Dynamic Obstacle Avoidance in Airspace
Generate real-time safe avoidance paths for drones and UAVs encountering dynamic obstacles such as other aircraft, structures, or terrain during flight. This could enable safe surveillance, surveying, and transport missions across wide airspaces, improving flight safety by over 75%.
Integration Roadmap — Estimated 18-Month Deployment
Data Integration & Requirements
Duration: 3 months
Establish the foundation for collecting driving behavior data from the licensee's fleet, integrating high-definition map information, and setting up API linkages with existing systems, while defining clear requirements.
Model Implementation & Optimization
Duration: 6 months
Implement this technology's inverse risk probability model and path generation algorithms to suit the licensee's environment, performing parameter tuning and optimization using real-world data.
Validation, Evaluation & Deployment
Duration: 9 months
Conduct pilot tests with a specific vehicle fleet to evaluate safety and effectiveness. Adjust based on validation results and progressively roll out to full production across the entire fleet.
Technical Feasibility
This technology is primarily algorithm-centric, leveraging vehicle driving behavior data and high-definition map information. It can acquire data from existing in-vehicle sensors (GPS, IMU, cameras, etc.) and communication modules, integrating as a software update into existing ECUs or telematics systems. This minimizes extensive hardware modifications, indicating a low technical barrier to adoption.
Success Scenario
Implementing this technology could integrate with commercial vehicle fleet management systems, providing optimal driving assistance tailored to driver fatigue levels and road conditions. This is expected to significantly reduce accident rates compared to existing systems, lessen driver stress, and enhance overall operational safety and efficiency. In the long term, it could lead to reduced insurance premiums and an estimated annual cost reduction of several hundred thousand dollars (AI est.).
Patent Record
APPLICATION NO.
特願2016-119270
REGISTRATION NO.
6664813
FILING DATE
2016年06月15日
GRANT DATE
2020年02月21日
EXPIRATION DATE
2036年06月15日
PATENT HOLDER
国立大学法人東京農工大学
Examination History
2019年02月12日
出願審査請求書
2020年02月04日
特許査定