Autonomous Lane Change Gap Viability Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicles require driver-initiated commands for lane changes, limiting their ability to proactively identify and execute lane changes based on available gaps in traffic flow.
Innovation Solution
A system and method that uses a radar system and processor to determine the viability of gaps between lanes by assessing alignment time, gap size, and relative velocity, allowing the vehicle to autonomously select and merge into suitable gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the autonomous vehicle requires driver-initiated commands for lane changes, then the driver maintains control and safety is improved, but the vehicle's ability to proactively identify and execute lane changes is limited
Solution Approach 1:
The system performs preliminary detection and evaluation of lane change gaps before driver action. The processor continuously monitors traffic patterns, identifies viable gaps, and pre-calculates lane change opportunities, so that when the driver requests a lane change, the system can immediately execute it using pre-identified gaps rather than searching for them in real-time.
Solution Approach 2:
The autonomous vehicle performs self-service by autonomously identifying, evaluating, and selecting lane change gaps without continuous driver intervention. The system monitors its own lane change opportunities, calculates viability metrics, and prepares execution plans, enabling it to serve its own navigation needs while maintaining driver oversight through the request-response interface.
2Productivity
If the vehicle continuously monitors and evaluates multiple gap options, then lane change opportunities are improved, but computational complexity and processing time increase
Solution Approach 1:
The system changes parameters by evaluating multiple gap viability metrics simultaneously (gap size, relative velocity, alignment time, safe zone dimensions) rather than sequentially. The processor calculates viability values for each gap using these transformed parameters, enabling comprehensive evaluation of multiple lane change opportunities in parallel without proportionally increasing processing time.
Solution Approach 2:
The monitoring and evaluation process is segmented into distinct functional modules: gap detection, viability calculation, gap selection, and execution planning. Each module handles a specific aspect of lane change opportunity identification, allowing the complex task to be divided into manageable computational segments that can be processed efficiently and independently.
3Reliability
If the vehicle aligns with the center of the selected gap, then merge safety is improved, but the alignment time and distance required increase
Solution Approach 1:
The system performs preliminary alignment calculations when the gap is first identified and evaluated. The processor determines the optimal alignment point within the gap and calculates the required steering and speed adjustments in advance, so that when the lane change is executed, the vehicle can follow a pre-computed trajectory to the gap center rather than reacting during the merge maneuver.
Solution Approach 2:
The alignment process is made dynamic by continuously adjusting the alignment target based on real-time conditions. As the vehicle approaches the gap, the system recalculates the optimal alignment point considering changing relative velocities, gap size variations, and traffic conditions, allowing the vehicle to adapt its alignment trajectory dynamically rather than following a fixed pre-determined path.
Data Source
AI summary
A vehicle, system and method for operating the vehicle is disclose. The system includes a radar system and a processor. The radar system locates a gap between targets in a second lane adjoining a first lane, with the host vehicle residing in the first lane. The processor is configured to determine a viability value of the gap for a lane change, select the gap based on the viability value, align the host vehicle with the selected gap, and merge the host vehicle from the first lane into the selected gap in the second lane.