Pedestrian BCI Signaling for Autonomous Vehicle Crossing Prediction
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Solution Overview
Problem
Autonomous vehicles lack effective means to seamlessly communicate with nearby pedestrians, leading to potential safety issues and inefficiencies in traffic flow.
Innovation Solution
Implementing a computer-implemented method that uses brain-computer interface (BCI) devices to receive, classify, and broadcast brainwave signals from nearby pedestrians to autonomous vehicles, enabling the vehicles to anticipate and respond to pedestrian movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicles use traditional sensing methods (cameras, LIDAR, radar) to detect pedestrians, then they can identify pedestrian positions, but they cannot anticipate pedestrian intentions or movements
Solution Approach 1:
The patent introduces brain-computer interface (BCI) devices as an intermediary between pedestrians and autonomous vehicles. These BCI devices capture brainwave signals from pedestrians and transmit them to the vehicle's processing system, enabling the vehicle to directly access the pedestrian's intended movements rather than just detecting their current position through traditional sensors.
Solution Approach 2:
The patent replaces traditional mechanical/optical sensing systems (cameras, LIDAR, radar) with a neuroscientific approach using brainwave detection. Instead of mechanically detecting pedestrian positions and inferring intentions, the system uses electrical signal detection from the brain to directly obtain movement intent information.
2Reliability
If autonomous vehicles process all received brainwave signals, then they can capture complete pedestrian information, but they cannot efficiently distinguish relevant movement signals from unrelated signals
Solution Approach 1:
The patent extracts only the relevant components from the complete brainwave signal spectrum. The processing system identifies and isolates specific signal patterns that correspond to movement intentions, filtering out unrelated neural activity. This extraction approach maintains reliability by focusing on movement-relevant signals while reducing processing complexity through selective analysis.
Solution Approach 2:
The patent applies different processing qualities to different portions of the brainwave signal data. Rather than uniformly processing all signals, the system identifies specific signal characteristics and patterns that locally indicate movement intent, applying enhanced processing only to those relevant portions while simplifying or discarding unrelated signal components.
3Loss of time
If autonomous vehicles react to pedestrian movements after detection, then they can respond to actual positions, but they cannot proactively anticipate and prevent potential conflicts
Solution Approach 1:
The patent enables preliminary action by obtaining brainwave signals that reveal pedestrian movement intentions before the actual movement occurs. The autonomous vehicle receives and processes these signals in advance, allowing it to anticipate upcoming actions and prepare appropriate responses, thereby reducing reaction time and improving traffic flow efficiency through proactive rather than reactive behavior.
Data Source
AI summary
Systems, methods and/or computer program products for improving autonomous vehicle operation by enabling communication between the autonomous vehicles and BCI systems publishing signals from nearby pedestrians. Wearable BCI devices worn by pedestrians analyze brainwave signals and classify the brainwave signals in order to filter out signals that are unrelated to crossing the street or the directionality of travel by the pedestrian. BCI devices publish, or broadcast brain wave signals related to crossing the street or directionality of travel to the surrounding area where autonomous vehicle receive and process the brainwave signals being published. The autonomous vehicles predict movements of nearby pedestrians based on the intended direction of travel signified by the collected brainwave signals, and the autonomous vehicles select driving actions in response to the anticipated movements of nearby pedestrians.