Autonomous Lane Change Detection with Dynamic ROI and Trajectory Prediction
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Solution Overview
Problem
Conventional autonomous vehicle systems are inadequate for safely navigating around prolonged blockages in the travel lane, as they often rely on insufficient longitudinal maneuvers that may lead to collisions when attempting to change lanes.
Innovation Solution
A lane change detection system that defines a dynamic region of interest based on the vehicle's speed and passenger comfort metrics, allowing the autonomous vehicle to assess and execute lane changes by considering the proximity and trajectory of surrounding objects, using multiple planning techniques to ensure safe passage into a destination lane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle performs lateral maneuvers to transition into an adjacent lane to circumvent a blockage, then the vehicle can continue its forward motion, but the vehicle risks colliding with other objects during the lane transition
Solution Approach 1:
The system performs preliminary detection of objects in the destination lane and predicts their future positions before executing the lane change maneuver. This advance planning allows the vehicle to assess collision risk and select a safe transition timing, thereby maintaining productivity while reducing the worsening feature of collision risk
Solution Approach 2:
The region of interest is dynamically adjusted based on vehicle speed, with its size and shape changing as a function of speed. This dynamic adaptation allows the system to optimize the detection area for lane change assessment, improving both the efficiency of the maneuver and the safety by considering speed-dependent spatial relationships
2Reliability
If the autonomous vehicle waits for the blockage to be removed before proceeding, then the vehicle avoids collision risk, but the vehicle experiences loss of time and reduced productivity
Solution Approach 1:
The system performs preliminary assessment of the destination lane by detecting objects and predicting their trajectories before the vehicle needs to make a decision. This advance analysis enables the vehicle to identify safe lane change opportunities without waiting for blockage removal, thereby reducing time loss while maintaining collision avoidance through predictive safety assessment
Solution Approach 2:
The system extends the region of interest beyond what would be minimally required for basic detection, creating a larger assessment area that allows earlier identification of safe lane change opportunities. This excessive detection coverage enables the vehicle to plan lane changes in advance, reducing waiting time while maintaining high safety standards
3Device complexity
If the autonomous vehicle uses a fixed-size region of interest for lane change detection, then the system is simpler to implement, but the system cannot adapt to varying vehicle speeds and passenger comfort requirements
Solution Approach 1:
The region of interest is defined with dynamic size and shape that change as a function of vehicle speed. This dynamic configuration allows the system to adapt to varying operating conditions, improving versatility while managing complexity through a systematic approach to region adjustment based on speed and comfort metrics
Data Source
AI summary
An autonomous vehicle configured to perform a lane change maneuver is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, radar, sonar, infrared, and GPS. The autonomous vehicle additionally includes a computing system that executes instructions on a lane change detection system and a control system. The lane change detection system includes a region of interest module and an object trajectory module for determining whether the autonomous vehicle will collide with another object if the autonomous vehicle maneuvers into an adjacent lane. An instruction module of the lane change detection system is further used to facilitate operational control of a mechanical system of the autonomous vehicle, such as an engine or a steering system.