Autonomous Lane Speed Estimation for Congestion-Aware Lane Changes
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying the lane with the fastest average travel speed, especially in heavy traffic conditions, which can exacerbate congestion when attempting to switch lanes.
Innovation Solution
An autonomous driving vehicle determines the average travel speed for each lane by tracking representative vehicles over a specified time period and planning a trajectory to move into the lane with the fastest speed, using a perception and planning system that includes sensors and data analytics to identify and adjust lane changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If vehicles weave through different lanes to find the fastest lane, then the average travel speed may improve, but the congestion is exacerbated
Solution Approach 1:
The system performs preliminary identification of the fastest lane using historical traffic data and real-time sensor information before the vehicle actually changes lanes. This advance planning allows the vehicle to move smoothly into the optimal lane without erratic weaving, thereby maintaining speed while reducing congestion-causing maneuvers
Solution Approach 2:
The system continuously monitors traffic conditions, lane speeds, and congestion levels, using this feedback to dynamically adjust lane change decisions. By responding to real-time feedback rather than constantly switching lanes, the system maintains optimal speed while avoiding exacerbation of congestion through excessive lane weaving
2Measurement precision
If the vehicle tracks and analyzes multiple vehicles over time to determine fastest lane, then the accuracy of lane selection improves, but the computation time and complexity increase
Solution Approach 1:
The system segments the task of determining fastest lane into distinct modules: sensor data acquisition, vehicle tracking, speed calculation, and lane selection. This segmentation allows each module to be optimized independently, improving measurement precision while managing system complexity through modular architecture
Solution Approach 2:
The system pre-processes and stores historical traffic data and vehicle tracking information, making this data readily available for quick analysis when lane change decisions are needed. This preliminary preparation improves the accuracy and speed of lane selection without increasing real-time computational complexity
Data Source
AI summary
A method, apparatus, and system for determining average lane travel speeds is disclosed. A plurality of vehicles traveling in a same direction as the ADV in a plurality of lanes are identified. Over a first time period, the plurality of vehicles is tracked. At least a first quantity of representative vehicles within the plurality of vehicles that are representative of vehicles traveling in the lane over the first time period are identified. For each of the plurality of lanes, an average speed over the first time period of the representative vehicles associated with the lane is determined. A trajectory is planned for the ADV, wherein the planned trajectory moves toward a lane whose representative vehicles have a fastest average speed. Thereafter, control signals are generated to control operations of the ADV based on the planned trajectory.