Multimodal In-Vehicle Violence Detection for Real-Time Threat Assessment
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Solution Overview
Problem
Current shared and pooled autonomous vehicle systems lack effective means to detect and respond to in-vehicle violence or threats, compromising passenger safety due to the inability to perceive and address potential physical and emotional risks during shared rides.
Innovation Solution
An in-vehicle multimodal violence detection system that integrates speech and non-speech audio event recognition, heart rate and breathing rate detection, and semantic scene recognition, generating threat indicators and utilizing a priori knowledge to assess and categorize threat levels, triggering appropriate responses such as activating vehicle horns, brake assist systems, and emergency assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple detection modules (audio, visual, physiological, vibration) are integrated to improve threat detection accuracy, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The system divides threat detection into four independent modular subsystems: audio event recognition module, semantic scene recognition module, physiological detection module, and vibration detection module. Each module independently processes specific threat indicators and outputs standardized threat level signals, enabling high detection precision through specialized sensing while managing complexity through functional decomposition and independent operation of each detection channel.
Solution Approach 2:
The system merges outputs from four different detection modules into a unified threat assessment through a threat determination unit. This consolidation integrates diverse threat indicators (audible, visual, physiological, vibration-based) into a single comprehensive threat level determination, achieving high measurement precision by combining multiple sensing modalities while presenting a unified interface that manages system complexity.
2Reliability
If comprehensive threat analysis with multiple indicators is implemented, then reliability of threat detection improves, but processing time and device complexity increase
Solution Approach 1:
The system continuously monitors all four threat indicators (audio events, semantic scenes, physiological signals, vibration patterns) in real-time and pre-processes this data through dedicated detection modules. This continuous preliminary analysis ensures that when a threat event occurs, the system already has processed threat indicators ready for immediate evaluation, improving reliability without adding significant delay during critical threat moments.
Solution Approach 2:
The threat determination unit receives continuous feedback from all detection modules and dynamically adjusts threat level assessments based on the combined state of all indicators. This feedback mechanism enables reliable real-time threat detection by continuously evaluating multiple indicators and updating threat levels as new information becomes available, balancing comprehensive analysis with responsive time-critical decision-making.
Data Source
AI summary
An in-vehicle violence detection system includes a speech and non-speech audio recognition module capturing occupant threat words and non-speech audio events. In-vehicle accelerometers generate data analyzed in a shaking movement recognition module. A heart rate and breathing rate detection module measures physiological changes in occupant heart rates and breathing rhythms. An in-vehicle semantic scene recognition module analyzes occupant non-verbal interactions. Occupant threat indicators including an audible threat indicator are generated by the speech and non-speech audio event recognition module. Visual threat indicators are generated by the in-vehicle semantic scene recognition module. Physiological threat indicators are generated by the heart rate and breathing rate detection module. Vibration-based threat indicators are generated by the shaking movement recognition module. The four threat indicators are fused to estimate a threat level, consolidated by incorporating contextual information including a vehicle location and time. Different actions are taken based on the consolidated threat level.