Vehicle Queue-Tail Estimation via Preceding-Vehicle Data
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Solution Overview
Problem
Existing vehicle motion planning systems face inefficiencies due to inaccurate estimation of the tail position of a vehicle queue, which can lead to suboptimal behaviors like getting stuck in traffic, as sensors are limited by detection range and blocked by preceding vehicles.
Innovation Solution
Systems and methods utilize vehicle sensors, cloud servers, and maps to determine an initial estimated position of a vehicle queue tail, estimate distance based on preceding vehicle data, and refine this position using moving averages to achieve a final accurate estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle sensors are used to detect the tail of the vehicle queue, then the detection capability is improved, but the detection range is limited and blocked by preceding vehicles
Solution Approach 1:
The patent uses the preceding vehicle as an intermediary to detect the tail of the vehicle queue. The sensor mounted on the preceding vehicle can detect the tail position without being blocked, and this information is then transmitted to the ego vehicle. This mediator approach allows the ego vehicle to obtain tail position information that would otherwise be inaccessible due to the blocking problem.
Solution Approach 2:
The patent shifts the detection dimension from the ego vehicle's perspective to the preceding vehicle's perspective. By mounting sensors on the preceding vehicle and using V2V communication, the system accesses tail position information from a different spatial dimension that is not blocked by the preceding vehicle's body, thereby overcoming the detection range limitation.
2Loss of information
If the ego vehicle uses its own sensor to detect the tail position, then the detection is direct, but the view is blocked by the preceding vehicle
Solution Approach 1:
The preceding vehicle acts as an intermediary that directly detects the tail position and communicates this information to the ego vehicle. This eliminates the view blockage problem because the sensor is mounted on the preceding vehicle where it has a clear line of sight to the tail, while the ego vehicle receives the information through V2V communication without needing direct detection.
3Productivity
If accurate tail position estimation is achieved, then motion planning efficiency is improved, but sensor limitations and obstructions must be overcome
Solution Approach 1:
The system establishes a feedback loop where the preceding vehicle continuously detects tail position information and transmits it to the ego vehicle via V2V communication. This real-time feedback enables the ego vehicle to continuously update its motion planning based on accurate tail position information, improving planning efficiency while overcoming sensor limitations through the feedback mechanism.
Solution Approach 2:
The preceding vehicle serves as a reliable intermediary that provides continuous tail position information through V2V communication. This intermediary approach ensures detection reliability because the sensor on the preceding vehicle operates without blockage, and the communication channel provides reliable data transmission to the ego vehicle for accurate motion planning.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable medium for estimating a position of a tail of a vehicle queue are provided. The methods include determining an initial estimated position of a tail of a vehicle queue comprising a plurality of vehicles relative to a beginning of a road section using vehicle sensors, estimating distance between a preceding vehicle and the tail of the vehicle queue based on driving data of the preceding vehicle, estimating a position of the tail based on the estimated distance and distance between an ego vehicle and the preceding vehicle, and estimating a final position of the tail based on the initial estimated position and the estimated position of the tail.