Autonomous Vehicle Lane Change Abort Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in safely and efficiently completing lane changes due to unpredictable changes in the behavior of surrounding objects, which can lead to unsafe conditions and reduced navigational effectiveness.
Innovation Solution
A computer-implemented method and system for an autonomous vehicle to initiate and abort lane changes based on data from sensors and predictive models, determining whether the lane change can be safely completed by analyzing changes in object behavior and adjusting the motion plan accordingly, allowing the vehicle to remain in the current lane or return to the original lane if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle initiates lane changes frequently to optimize routing, then travel efficiency is improved, but the risk of unsafe conditions increases due to unpredictable changes in surrounding objects
Solution Approach 1:
The system performs preliminary safety checks before initiating lane changes by predicting future positions of surrounding objects and evaluating potential hazards in advance. This allows the vehicle to optimize routing while maintaining safety by only initiating lane changes when predictive analysis confirms safe conditions.
Solution Approach 2:
The system dynamically adjusts lane change initiation based on real-time sensor data and predictive modeling of surrounding objects. The safety parameters and routing decisions are continuously updated as the vehicle's environment changes, allowing flexible optimization while adapting to unpredictable object behavior.
2Reliability
If the autonomous vehicle is conservative in initiating lane changes to ensure safety, then safety is improved, but travel efficiency and navigational effectiveness deteriorate
Solution Approach 1:
The system uses feedback from sensor data and predictive models to dynamically adjust lane change decisions. By continuously monitoring surrounding objects and updating safety assessments, the system can confidently initiate lane changes when conditions permit, improving travel efficiency without compromising safety through conservative restrictions.
Solution Approach 2:
The system changes safety parameters and risk thresholds based on predictive analysis of surrounding objects. When predictions indicate low risk, the system allows more aggressive lane change initiation; when risks are detected, parameters are adjusted to prevent unsafe changes, optimizing the balance between safety and efficiency.
3Reliability
If the autonomous vehicle monitors surrounding objects continuously to detect changes, then safety is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The system extracts only the critical changes in surrounding objects that affect lane change safety, rather than processing all sensor data continuously. By focusing computational resources on detecting and responding to specific hazardous changes, the system maintains high safety monitoring without excessive computational complexity.
Solution Approach 2:
The system performs preliminary predictive analysis to identify objects and trajectories that are likely to become hazards. This pre-filtering allows continuous monitoring to focus on predicted risk areas, maintaining comprehensive safety detection while reducing overall computational burden by not processing all data at full resolution.
Data Source
AI summary
Systems and methods are directed to lane change control for an autonomous vehicle. In one example, a computer-implemented method for determining whether to abort a lane change in an autonomous vehicle includes initiating, by a computing system comprising one or more computing devices, a lane change procedure for an autonomous vehicle. The method further includes obtaining, by the computing system, data indicative of one or more changed objects relative to the autonomous vehicle. The method further includes determining, by the computing system, that the lane change procedure cannot be completed by the autonomous vehicle based at least in part on the data indicative of one or more changed objects. The method further includes in response to determining that the lane change cannot be completed by the autonomous vehicle, generating, by the computing system, a motion plan that controls the autonomous vehicle to abort the lane change procedure.