Autonomous Vehicle Traffic Light Inference via Range Sensors
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Solution Overview
Problem
Current methods for inferring the state of a traffic signal at an intersection, such as visual observation or movement of other objects, are inefficient and may not accurately determine the traffic signal state, particularly for autonomous vehicles navigating through intersections.
Innovation Solution
Implementing range sensors, such as LiDAR or RADAR, to determine the movement state of objects at the intersection, allowing the autonomous vehicle to infer the traffic signal state and control its operation accordingly, potentially augmented with vision-based sensors for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual observation or movement of other objects is used to infer traffic signal state, then the method is simple to implement, but the accuracy and reliability of determining traffic signal state deteriorates
Solution Approach 1:
The patent introduces range sensors (LiDAR, RADAR) as intermediary devices that indirectly detect traffic signal state by measuring movement states of objects at the intersection. Instead of directly observing traffic lights or relying on visual cues, the system uses electromagnetic radiation to detect object movement, which then infers the traffic signal state. This intermediary approach resolves the contradiction by providing accurate measurement through physical sensing while maintaining implementation simplicity through automated processing.
Solution Approach 2:
The patent replaces visual observation methods with electromagnetic-based range sensing technology. Instead of using cameras or human visual inspection to detect traffic signal states, the system employs LiDAR or RADAR to measure object movement through electromagnetic radiation time-of-flight or Doppler effects. This substitution improves measurement precision by using physical measurement principles rather than visual interpretation.
2Measurement precision
If range sensors are used to determine movement state of objects, then the accuracy of traffic signal state determination improves, but the device complexity increases
Solution Approach 1:
The patent applies range sensors that serve multiple functions: they detect object presence, measure object movement state, determine distance to intersection, and infer traffic signal state. By using a single sensor type for multiple detection purposes, the system improves measurement precision without proportionally increasing device complexity. The same LiDAR or RADAR hardware performs various measurement tasks that would otherwise require separate sensor systems.
Solution Approach 2:
The range sensors automatically perform detection and measurement without requiring additional processing systems. The sensor data is directly used by the planning circuit to determine traffic signal state, creating a self-sufficient detection system. This reduces the overall system complexity by eliminating the need for separate visual observation systems or manual intervention, as the range sensors self-service the entire detection and inference process.
3Device complexity
If visual observation methods are used, then the system complexity remains low, but the productivity and efficiency of navigation through intersections deteriorates
Solution Approach 1:
The patent replaces manual or visual observation processes with automated electromagnetic sensing and electronic processing. Range sensors continuously measure object movement states and the planning circuit automatically determines traffic signal states, enabling faster and more reliable decision-making. This substitution improves productivity by eliminating the delays and inaccuracies associated with visual observation while maintaining manageable system complexity through integrated electronic systems.
Solution Approach 2:
The system implements continuous feedback by using range sensors to monitor object movement states in real-time and automatically adjusting navigation decisions based on inferred traffic signal states. This feedback loop enables efficient intersection navigation by providing up-to-date information about traffic conditions and signal states, allowing the vehicle to respond quickly and appropriately without the delays inherent in visual observation methods.
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
Enables accurate and efficient determination of traffic signal states, enabling autonomous vehicles to navigate intersections safely and efficiently by using sensor data to infer traffic signal conditions without relying solely on visual observations.
Implementation Method 1
A planning circuit of an autonomous vehicle receives information sensed by a range sensor of the autonomous vehicle. The information represents a movement state of an object through the intersection.
Implementation Method 2
Implementing range sensors, such as LiDAR or RADAR, to determine the movement state of objects at the intersection
Implementation Method 3
Implementing range sensors, such as LiDAR or RADAR, to determine the movement state of objects at the intersection
Implementation Method 4
Implementing range sensors, such as LiDAR or RADAR
Data Source
AI summary
Among other things, we describe techniques for traffic light estimation using range sensors. A planning circuit of a vehicle traveling on a first drivable region that forms an intersection with a second drivable region receives information sensed by a range sensor of the vehicle. The information represents a movement state of an object through the intersection. A traffic signal at the intersection controls movement of objects through the intersection. The planning circuit determines a state of the traffic signal at the intersection based, in part, on the received information. A control circuit controls an operation of the vehicle based, in part, on the state of the traffic signal at the intersection.