Multi-modal Interactive Intelligent Cockpit:
The multi-modal interactive intelligent cockpit is a human-machine interaction scenario in the automobile cockpit. It integrates driving assistance systems, entertainment systems, information security systems, etc., with the Internet. It utilizes algorithm systems to process massive information data and offers users an efficient, intuitive, and futuristic driving experience through various multi-modal interaction methods such as voice recognition, facial recognition, and gesture recognition. It aims to actively understand and meet user needs, enhance the driving and riding experience, while ensuring user safety and comfort.
Main components: central control screen, voice recognition system, gesture recognition system, physical buttons and knobs, head-up display, audio prompts and warnings, intelligent driving assistance system, in-car cameras.
PointSpread self-developed gesture recognition system includes high-precision TOF cameras and self-developed algorithms. It not only accurately recognizes the driver's gesture information but also possesses self-growing capabilities, contributing to the multi-modal interactive intelligent cockpit.
TOF Camera Self-Growth Algorithm System:
The TOF camera algorithm system has the ability to autonomously improve and enhance its own performance through learning and accumulated experience. It continuously optimizes its algorithms, models, or strategies to adapt to new tasks, environments, or data.
Self-growth implementation process:
- Data Collection: The TOF camera continuously collects user data during the driving process, including distance, depth, motion, enabled functions, etc., and transmits them to the algorithm system for processing.
- Data Analysis and Learning: The algorithm system analyzes and learns from the collected data. It utilizes machine learning and deep learning technologies to extract features, recognize patterns, and build models to understand the environment and driver behavior.
- Real-time Updates and Incremental Learning: As the driver's usage increases, the algorithm system constantly receives new data and compares and updates it with previous data. It can fine-tune the parameters of the model by using new data samples or new tasks without retraining the entire model, to reflect new knowledge and experience. Through real-time updates, the algorithm system can continuously optimize its models and algorithms to improve recognition accuracy and efficiency.
- Adaptation and Optimization: The algorithm system adapts and optimizes based on the driver's usage habits and feedback. It can adjust according to the driver's personalized needs and preferences to provide a better driving experience and interaction effect.
Through continuous data collection, analysis, and learning, the TOF camera and algorithm system can continuously improve their functionality and performance, achieving the capability of self-growth. This capability allows the system to gradually adapt to different driving scenarios and optimize based on real-world usage, providing drivers with a more intelligent and precise driving experience.