Probabilistic Grid Obstacle Detection via Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Limited visibility during vehicle operations, such as backing up, increases the risk of collisions with obstacles due to restricted visibility for drivers or autonomous driving systems.
Innovation Solution
A vehicle control system incorporating an obstacle detection system that utilizes a combination of sensors like cameras, LIDAR, radar, and sensor fusion to create a probabilistic grid-based map, enabling the detection of obstacles and predicting potential collisions, and alerting the driver or autonomous system to take necessary actions to avoid obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and sensor fusion are used to improve obstacle detection accuracy, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (cameras, LIDAR, radar) into a unified sensor fusion system that processes data from all sources to create a comprehensive probabilistic grid-based map of obstacles. This merging approach improves measurement precision by cross-validating detections across multiple sensors while managing complexity through integrated processing architecture.
Solution Approach 2:
The sensor fusion system serves multiple functions simultaneously: it detects obstacles, classifies them by type, determines their trajectories, predicts future positions, and generates probabilistic occupancy maps. This multi-functionality improves detection accuracy across various scenarios while avoiding the need for separate specialized systems for each function.
2Reliability
If real-time data processing from multiple sensors is performed, then obstacle detection speed and reliability improve, but use of energy increases
Solution Approach 1:
The system continuously processes sensor data in real-time to maintain an up-to-date probabilistic grid-based map of the environment. This continuous processing ensures reliable obstacle detection as the vehicle moves, with the system constantly updating obstacle positions, trajectories, and probabilities without interruption to maintain safety.
Solution Approach 2:
The sensor fusion system incorporates feedback mechanisms where detection results from previous time steps inform current processing. The probabilistic grid map is updated iteratively using Bayesian filtering, where prior probabilities are continuously refined with new sensor measurements, improving reliability while optimizing energy use through efficient recursive processing rather than complete re-processing.
3Measurement precision
If a probabilistic grid-based map is created and updated in real-time, then obstacle detection accuracy improves, but loss of time for processing increases
Solution Approach 1:
The system divides the environment into a grid-based spatial map where each cell represents a discrete location. This segmentation allows parallel processing of sensor data across different grid cells, improving computational efficiency. Each grid cell can be processed independently to determine obstacle probability, reducing overall processing time while maintaining accurate spatial representation.
Solution Approach 2:
The system pre-establishes the probabilistic grid-based map structure and data structures before actual obstacle detection begins. By preparing the computational framework in advance with predefined grid cells and probability initialization, the system reduces real-time processing requirements during actual obstacle detection, as the infrastructure is already in place for rapid updates.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively enhances safety by accurately detecting obstacles and preventing collisions through real-time data processing and alerting mechanisms, ensuring safe navigation in restricted visibility conditions.
Implementation Method 1
A vehicle control system incorporating an obstacle detection system that utilizes a combination of sensors like cameras, LIDAR, radar
Implementation Method 2
A vehicle control system incorporating an obstacle detection system that utilizes a combination of sensors like cameras, LIDAR, radar
Data Source
AI summary
Example obstacle detection systems and methods are described. In one implementation, a method receives data from at least one sensor mounted to a vehicle and creates a probabilistic grid-based map associated with an area near the vehicle. The method also determines a confidence associated with each probability in the grid-based map and determines a likelihood that an obstacle exists in the area near the vehicle based on the probabilistic grid-based map.