Autonomous Vehicle Gesture Communication for Intersection Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating complex driving scenarios, such as intersections, where human communication through gestures is difficult to replicate, leading to potential collisions or traffic deadlocks.
Innovation Solution
A computer-implemented method for autonomous vehicles that generates predicted trajectories based on sensor data, detects and emits gestures to communicate with other vehicles, and modifies these trajectories based on detected and emitted gestures to make driving decisions, including yielding or overtaking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use sensor data and trajectory prediction to navigate intersections, then collision avoidance capability is improved, but the ability to handle complex social driving scenarios (gesture communication) deteriorates
Solution Approach 1:
The patent applies the copying principle by replicating human gesture communication behaviors in autonomous vehicles. The system detects gestures from other vehicles and emits corresponding gestures in response, copying human-like social interaction patterns to resolve right-of-way situations at intersections without requiring actual human drivers.
Solution Approach 2:
The patent uses gesture emission as an intermediary communication mechanism between autonomous vehicles. Instead of directly negotiating complex driving scenarios through multiple sensors and processors, the system introduces gesture signals as a simplified mediator that conveys intent (yield or overtake) between vehicles, making social driving scenarios more manageable.
2Adaptability or versatility
If autonomous vehicles detect and respond to gestures from other vehicles, then adaptability in social driving scenarios is improved, but system complexity increases
Solution Approach 1:
The patent extracts the essential communication function from complex human interaction and isolates it into specific gesture signals (yield gesture and overtake gesture). By taking out only the necessary gesture detection and emission capabilities rather than implementing full human-like communication, the system achieves adaptability while controlling complexity.
Solution Approach 2:
The patent segments gesture communication into distinct, predefined gesture types (yield gesture for allowing other vehicles to pass, overtake gesture for signaling intent to overtake). This segmentation allows the system to handle complex social scenarios through a limited set of discrete signals rather than continuous complex communication, reducing system complexity.
3Productivity
If autonomous vehicles emit gestures to communicate intent, then traffic flow efficiency is improved, but the risk of miscommunication or unrecognized gestures increases
Solution Approach 1:
The patent applies beforehand cushioning by preparing multiple predicted trajectories with associated probabilities before gesture emission. The system has pre-computed possible driving paths (including yielding and overtaking options) with their likelihoods, so when gestures are emitted or detected, the vehicle can quickly select from pre-prepared trajectories rather than computing from scratch, cushioning against communication delays or misunderstandings.
Solution Approach 2:
The patent implements feedback by continuously monitoring detected gestures from other vehicles and adjusting trajectory selection accordingly. The system detects gestures, modifies predicted trajectories based on detected and emitted gestures, and re-evaluates probabilities, creating a feedback loop that improves reliability by adapting to actual gesture recognition outcomes rather than operating with fixed pre-planned paths.
Data Source
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AI summary
Responsive to sensor data received from one or more sensors of an autonomous vehicle, one or more predicted trajectories are generated, with each of the predicted trajectories having an associated probability. One or more driving scenarios that trigger gesture recognition are identified. For each of the identified driving scenarios, one or more gestures from one or more vehicles are detected in accordance with a gesture detection protocol. One or more gestures from the autonomous vehicle are emitted for communication with the vehicles in accordance with a gesture emission protocol based on the detected gestures. The predicted trajectories are modified based on the detected gestures, the emitted gestures and the associated probabilities of the predicted trajectories. The autonomous vehicle is controlled based on the modified predicted trajectories.