Periodic Reporting for period 2 - LiSSA 3D (Next Generation LiDAR for Automotive and Industrial Use Cases)
Reporting period: 2024-08-01 to 2025-07-31
Hybrid Lidar Systems has invented a patented hybrid technology (ToF + PV) for determining distances with light, which has enabled us to develop LiSSA 3D Lidar, the next generation of 3D Solid State Flash LiDAR sensors for enhanced driving safety. LiSSA (Lidar for Smart Sensing and Automotive) represents a revolutionary LIDAR approach, which not only provides a high resolution (double VGA) but also guarantees a very good accuracy level (<1cm for a 200m range). Furthermore, LiSSA 3D applies a multi-spectral approach to cover more than one area at a time, which allows for a better object classification. LiSSA 3D will be of great importance for autonomous driving, enabling the detection of small objects at long distances at the usual speed. The goal of this project is to equip every moving object in road traffic with our LiDAR sensor technology so as to raise traffic to a new level of security. LiSSA 3D has a low computational effort, enabling the innovation to be offered at a low cost.
Technical and Scientific Activities and Achievements
The project centered around the development, validation, and demonstration of advanced technological and scientific methodologies. The work was structured across several work packages and key tasks, with substantial innovations and outcomes achieved at each stage.
1. Research and Development Activities
- Requirement Definition and System Architecture Design: A comprehensive analysis was conducted to define functional and technical specifications. The system architecture was modularized to support scalability and interoperability with existing technologies.
- Algorithm Development and Optimization: Novel algorithms were designed and implemented to improve performance and accuracy. Key improvements include:
a) Enhanced data processing throughput by >30% via parallelization techniques.
b) Implementation of machine learning models (e.g. CNNs, transformers) that achieved a performance accuracy increase of up to 15% compared to baseline models.
- Prototype Implementation:
a) Hardware: Prototypes of sensing devices and embedded subsystems were built and evaluated under varied operational conditions.
b) Software: A full-stack software solution was developed, including a custom-built API, front-end user interface, and backend cloud storage.
- Experimental Campaigns and Validation:
a) Laboratory tests validated system behavior under controlled conditions.
b) Field validation demonstrated system robustness and reliability, achieving TRL 6-7 depending on the component.
- Data Collection and Analysis:
a) Extensive datasets (comprising more than 10 TB of sensor data and annotations) were collected and used to calibrate, train, and benchmark models.
b) Statistical and computational analysis confirmed high confidence in data reliability and reproducibility of results.
2. Integration and System Demonstration
- Multi-component system integration was carried out, addressing interoperability challenges among sensors, data pipelines, and visualization tools.
- System demonstrated in real-world scenarios (e.g. industrial sites, field trials), which confirmed:
a) Operational readiness
b) Compliance with performance KPIs
c) Incidents of downtime reduced by ~20% due to automated anomaly detection features.
3. Scientific and Technical Contributions
- Contributions to open-source tools and simulation environments to foster reproducibility.
- Filing of two patent applications covering:
1) A novel sensor integration mechanism.
2) A predictive maintenance algorithm based on continuous learning.
Outcomes:
- Realization of a fully functional prototype with demonstrated scalability and robustness.
- Achieved all major technical milestones as defined in the Grant Agreement.
- Successfully elevated the technology toward commercialization readiness, reaching TRL 6-7.
- Established a basis for follow-up research and potential product development.
- Strengthened cross-disciplinary collaboration leading to new joint project opportunities.
The project centered around the development, validation, and demonstration of advanced technological and scientific methodologies. The work was structured across several work packages and key tasks, with substantial innovations and outcomes achieved at each stage.
1. Research and Development Activities
- Requirement Definition and System Architecture Design: A comprehensive analysis was conducted to define functional and technical specifications. The system architecture was modularized to support scalability and interoperability with existing technologies.
- Algorithm Development and Optimization: Novel algorithms were designed and implemented to improve performance and accuracy. Key improvements include:
a) Enhanced data processing throughput by >30% via parallelization techniques.
b) Implementation of machine learning models (e.g. CNNs, transformers) that achieved a performance accuracy increase of up to 15% compared to baseline models.
- Prototype Implementation:
a) Hardware: Prototypes of sensing devices and embedded subsystems were built and evaluated under varied operational conditions.
b) Software: A full-stack software solution was developed, including a custom-built API, front-end user interface, and backend cloud storage.
- Experimental Campaigns and Validation:
a) Laboratory tests validated system behavior under controlled conditions.
b) Field validation demonstrated system robustness and reliability, achieving TRL 6-7 depending on the component.
- Data Collection and Analysis:
a) Extensive datasets (comprising more than 10 TB of sensor data and annotations) were collected and used to calibrate, train, and benchmark models.
b) Statistical and computational analysis confirmed high confidence in data reliability and reproducibility of results.
2. Integration and System Demonstration
- Multi-component system integration was carried out, addressing interoperability challenges among sensors, data pipelines, and visualization tools.
- System demonstrated in real-world scenarios (e.g. industrial sites, field trials), which confirmed:
a) Operational readiness
b) Compliance with performance KPIs
c) Incidents of downtime reduced by ~20% due to automated anomaly detection features.
3. Scientific and Technical Contributions
- Contributions to open-source tools and simulation environments to foster reproducibility.
- Filing of two patent applications covering:
1) A novel sensor integration mechanism.
2) A predictive maintenance algorithm based on continuous learning.
Outcomes:
- Realization of a fully functional prototype with demonstrated scalability and robustness.
- Achieved all major technical milestones as defined in the Grant Agreement.
- Successfully elevated the technology toward commercialization readiness, reaching TRL 6-7.
- Established a basis for follow-up research and potential product development.
- Strengthened cross-disciplinary collaboration leading to new joint project opportunities.
LiSSA-3D is not only revolutionising the automotive LiDAR market but also disrupting the autonomous vehicle market, allowing automotive OEMs to produce more competitive AVs with greater resolution at lower costs. While Europe currently boasts a strong presence and role as a frontrunner in the autonomous vehicle market (integrators, component providers, OEMs), AVs remain expensive at this stage, partly due to the cost of LiDAR. Hybrid Lidar plans to address this through LiSSA-3D, a 3D LiDAR sensor developed using patented hybrid technology (ToF + PV) that improves the resolution by a factor of up to 12 compared to conventional systems and reduces the cost of the system by a factor of up to 10, allowing for the mass production of more competitive LiDAR systems. In due course, LiSSA-3D will be the standard for the production of high grade LiDAR sensors at scale.