Vehicle Collision Avoidance via Sensor Fusion and Driver Intent

Solution Overview

Problem

Current collision mitigation systems in vehicles are ineffective at utilizing various data sources and struggle to accurately predict and prevent collisions at intersections, particularly due to their inability to selectively use relevant data effectively.

Innovation Solution

A vehicle-based system that employs a network of data collectors, including radar, lidar, cameras, and communication technologies, to determine potential collision trajectories and activate safety systems such as braking, steering, and warning systems by integrating data from various sensors and remote sources to predict and prevent collisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If current collision mitigation systems use available data sources, then collision detection capability is provided, but the systems are unable to effectively utilize various data sources and make good use of available data

Engineering Contradiction:

Improvedata utilization effectivenessVSAvoidcollision detection accuracy

Core Design Contradiction:

Loss of informationVSReliability

Solution Approach 1:

The system segments data processing by creating separate functional modules: data collection module that gathers raw data from multiple sensors, data fusion module that integrates multi-source data, trajectory prediction module that analyzes potential collision paths, and mitigation control module that executes avoidance maneuvers. This segmentation allows each module to optimize its specific function while collectively improving overall data utilization effectiveness and collision detection accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system merges data from heterogeneous sources including radar, lidar, cameras, GPS, and vehicle sensor networks into a unified collision assessment framework. By combining these diverse data streams through advanced fusion algorithms, the system overcomes the limitations of individual sensors and achieves comprehensive situational awareness, thereby improving both data utilization effectiveness and collision detection reliability.

Inventive Principle:

Principle #5Merging (Combining)

2Measurement precision

If collision mitigation systems implement comprehensive data integration, then collision prediction accuracy improves, but system complexity and implementation cost increase

Engineering Contradiction:

Improvecollision prediction accuracyVSAvoidsystem implementation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary data preprocessing and filtering before full collision analysis, extracting only relevant features from raw sensor data. It pre-establishes collision risk thresholds and prediction models based on historical data, so that during operation, the system can quickly assess collision risks without processing all raw data in real-time. This preliminary action reduces computational complexity while maintaining high prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies different processing qualities to different data sources based on their reliability and relevance to specific collision scenarios. High-priority data from critical sensors receives more intensive processing, while less critical data undergoes lighter processing. This localized quality approach optimizes computational resources while maintaining overall prediction accuracy and reducing system complexity.

Inventive Principle:

Principle #3Local quality

3Productivity

If the system selectively uses relevant data sources, then data processing efficiency improves, but the ability to detect all potential collision risks may be reduced

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidcollision risk detection completeness

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system dynamically adjusts data source selection based on real-time driving conditions, vehicle state, and detected environmental context. During normal driving, it processes data from essential sensors; when potential risks are detected, it activates additional sensors and data streams. This dynamic adaptation maintains processing efficiency while ensuring comprehensive risk detection when needed, resolving the contradiction between efficiency and completeness.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback mechanisms where collision prediction results and actual collision outcomes are continuously fed back to refine data selection criteria. The system learns from past performance to optimize which data sources to prioritize under different conditions, improving processing efficiency while maintaining detection completeness through adaptive refinement of its data usage strategy.

Inventive Principle:

Principle #23Feedback

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

The system effectively predicts potential collisions by generating a digital road network map and determining threat levels, allowing for timely activation of safety measures to prevent accidents, enhancing intersection safety and reducing collision risks.