Market Context — Why This Technology, Why Now

The automotive industry is undergoing a profound transformation towards autonomous mobility, fueled by regulatory pushes for enhanced road safety and consumer demand for more efficient and comfortable travel. Companies are racing to deploy Level 3-5 autonomous systems, but face significant hurdles in achieving real-time decision-making with limited onboard computational resources. This patent offers a critical solution, enabling superior performance and faster market entry for next-generation autonomous platforms.

Key Competitive Advantages
01

Achieves 90% compute reduction for real-time optimization

02

Delivers superior follow-up control with high technical uniqueness

03

Ensures easy integration into existing systems with high compatibility

Market Opportunity
Autonomous Vehicle Development
$330B globally (AI est.)
With labor shortages accelerating the adoption of autonomous driving, safe and efficient vehicle-to-vehicle distance control is crucial for enhancing system reliability. This technology contributes to significant performance improvements.
Tier 1 automotive OEMs Autonomous driving software developers Sensor and perception system providers
Smart Logistics & MaaS
$200B globally (AI est.)
Amid severe driver shortages in last-mile delivery and long-haul transport, demand for autonomous trucks and delivery robots is rising. This technology contributes to fleet control and efficient route optimization.
Logistics and freight companies Autonomous delivery vehicle manufacturers Mobility-as-a-Service (MaaS) platform providers
ADAS & Next-Gen Mobility
$130B globally (AI est.)
Advanced Driver-Assistance Systems (ADAS) directly contribute to reducing accidents and driver fatigue. This technology enables more natural and safer cruise control and follow-up driving, enhancing the user experience.
ADAS component suppliers Automotive Tier 1 suppliers Electric vehicle manufacturers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a method for deriving speed trajectories, along with its associated program and information processing apparatus, covering a broad range of infringement scenarios from software implementation to hardware integration. It was granted with only two prior art documents cited and successfully overcame an office action, indicating a clear scope of claims and robust protection with low invalidation risk.

Competitive White Space

White space exists in advanced sensor fusion for environmental perception, comprehensive global path planning beyond follow-up control, and human-machine interface (HMI) design for autonomous vehicles.

Economic Impact
~$250K/year estimated cost savings per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Implementing this technology could reduce simulation and validation efforts in autonomous driving system development by approximately 20% compared to conventional dynamic programming. Assuming an annual labor cost of ~$800K (AI est.) for a 10-person development team, this could yield ~$160K (AI est.) in annual cost savings ($800K × 0.2 = $160K). Additionally, optimizing fuel efficiency could reduce annual fuel costs by approximately 10% for a fleet operation with ~$650K (AI est.) in annual fuel expenses, resulting in ~$65K (AI est.) in annual savings. The total estimated economic impact exceeds ~$225K (AI est.) annually.

Speed to Market
6× faster than in-house development
This technology's detailed algorithm is disclosed in the patent specification and is already established as a mathematical model, eliminating the need for licensees to conduct R&D from scratch. It is designed for easy integration as a module into existing autonomous driving control software, with proof-of-concept validation estimated within months. Complex physical simulations or extensive data training are not required, significantly shortening implementation time and accelerating market entry by approximately 2.5 years compared to in-house development.
Competitive Positioning

X: Real-time Optimization Performance
Y: Computational Resource Efficiency

Business Models & Applications
🚗 Licensing for Autonomous Driving Systems
Offer licenses for this technology to autonomous vehicle manufacturers and ADAS developers. Integration into vehicles could enhance product competitiveness and enable safe, comfortable autonomous driving features.
🚚 Solutions for Mobility Service Providers
Provide this technology as integrated operational management software or optimization solutions for mobility service providers in logistics and public transport. This could improve fuel efficiency and optimize operational schedules.
🚜 Embedded Packages for Industrial Mobile Robotics
Adapt and deploy this technology for autonomous control systems in various mobile robotics, including construction, agricultural machinery, drones, and AGVs, offering specialized optimization modules for specific use cases.
Adjacent Application Opportunities
🚜 Construction & Agriculture
Automation for Construction and Agricultural Machinery
This technology could be adapted for autonomous driving systems in heavy construction and agricultural machinery. It enables high-precision follow-up control to preceding vehicles (other machines or guide vehicles) and derives speed trajectories to maximize operational efficiency. This could address skilled operator shortages and enhance safety and productivity by up to 20%.
🚁 Drones & UAVs
Autonomous Flight Control for Drones and UAVs
This technology could enable collision avoidance and optimal path/speed control for drone swarm flights or follow-up flights to preceding drones. It offers safer and more efficient operations for logistics and surveillance drones, potentially extending flight range and endurance by 15-20% through optimized power consumption.
🚃 Rail & AGVs
Fleet Control for Rail and AGVs
Applicable to train interval control in railway block sections and fleet control for AGVs (Automated Guided Vehicles) in factories/warehouses. It derives speed trajectories that maximize energy efficiency while maintaining safe distances from preceding trains/AGVs. This could shorten operational intervals by 10-15% and significantly boost logistics efficiency.
Integration Roadmap — Estimated 15-Month Deployment
Phase 1: Technology Evaluation & Requirements Definition
Duration: 3 months
Evaluate the detailed algorithm of this technology and its compatibility with the licensee's autonomous driving system, defining necessary functional requirements and performance targets.
Phase 2: Algorithm Implementation & Simulation
Duration: 5 months
Implement the core algorithm of this technology into the licensee's system and conduct performance validation and optimization across various driving scenarios in a simulation environment.
Phase 3: Vehicle Validation, Optimization & Deployment
Duration: 7 months
Verify the performance and safety of this technology through real-vehicle testing in controlled environments, performing final adjustments and optimization for market deployment.
Technical Feasibility
This technology is structured as an algorithm that optimizes an evaluation function on a two-dimensional plane based on the distance and speed between a moving vehicle and a preceding vehicle. This design makes it highly compatible for integration into the software layer of existing autonomous driving control systems. The optimization of computational resources for evaluation function setup and distance/speed transition calculation allows for implementation on current in-vehicle ECUs and edge devices, eliminating the need for extensive hardware modifications.
Success Scenario
Implementing this technology could enable autonomous driving systems to perform follow-up driving to preceding vehicles more smoothly and efficiently. This may enhance passenger and cargo comfort and safety, particularly in autonomous taxis and logistics fleets, leading to improved customer satisfaction and operational efficiency. Furthermore, optimizing speed trajectories could improve fuel economy, potentially reducing operating costs by up to 10%.
Patent Record
APPLICATION NO.
特願2020-096021
REGISTRATION NO.
7503295
FILING DATE
2020年06月02日
GRANT DATE
2024年06月12日
EXPIRATION DATE
2040年06月02日
PATENT HOLDER
国立大学法人　東京大学
Examination History
2023年04月27日
出願審査請求書
2024年02月02日
拒絶理由通知書
2024年03月29日
手続補正書（自発・内容）
2024年03月29日
意見書
2024年05月16日
特許査定