Autonomous Vehicle Lane Reduction Risk Assessment and Collision Avoidance
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating lane reductions safely, as existing systems lack effective methods to detect neighboring vehicle intentions and adjust speed and trajectory in real-time to prevent collisions, especially in high-speed or high-traffic areas.
Innovation Solution
A system comprising a processor, computer vision module, and communication module that detects lane reductions, neighboring vehicle actions, and license plates, determines time intervals, and executes risk assessments to transmit control commands to actuators, enabling the host vehicle to adjust its speed and trajectory to avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use basic lane detection systems, then the system complexity is low, but the ability to detect neighboring vehicle intentions and prevent collisions in lane reductions is insufficient
Solution Approach 1:
The system segments the detection task into multiple specialized modules: lane reduction detection module detects upcoming lane reductions, neighboring vehicle detection module identifies vehicles in adjacent lanes, and intention prediction module forecasts vehicle actions. This segmentation allows each module to specialize in specific detection functions, improving overall reliability while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary detection of lane reductions and neighboring vehicles before the actual collision risk materializes. By detecting lane reductions in advance and predicting neighboring vehicle intentions before they execute maneuvers, the system has sufficient time to plan and execute avoidance maneuvers, significantly improving collision prevention capability.
2Reliability
If the system performs comprehensive risk assessment including neighboring vehicle detection and intention prediction, then the collision prevention capability is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary detection of lane reductions and neighboring vehicles well in advance of the critical decision point. By identifying potential hazards early in the timeline, the system can initiate risk assessment procedures with sufficient time, reducing the urgency and allowing more comprehensive analysis without excessive time loss.
Solution Approach 2:
The system implements real-time processing that rapidly evaluates detected hazards and predicts vehicle intentions using optimized algorithms. By rushing through the computational steps with efficient processing, the system maintains comprehensive risk assessment capability while minimizing the actual processing time to meet real-time requirements.
3Reliability
If the system adjusts vehicle speed and trajectory in real-time based on detected risks, then the collision avoidance effectiveness is improved, but the control system complexity and actuator requirements increase
Solution Approach 1:
The system dynamically adjusts vehicle speed and trajectory in real-time based on detected risks and predicted intentions. The control commands are continuously updated as new information becomes available, allowing the vehicle to adapt its motion profile dynamically. This dynamic control approach maximizes collision avoidance effectiveness while using standard actuator systems.
Solution Approach 2:
The system implements a feedback loop where detection results inform risk assessment, which generates control commands that are executed by actuators, and the results are monitored to refine subsequent detection and assessment. This closed-loop feedback mechanism ensures that control adjustments are effective and can be refined in real-time, improving collision avoidance without requiring overly complex open-loop control systems.
Data Source
Figure 1A~1B
Figure 2A~2C
Figure 3A
AI summary
An embodiment related to a system (300) comprising a processor (304), a computer vision module (306), and a control module (310); wherein the processor (304) is operable to detect a lane reduction in a nearest disappearing lane (902); detect a neighboring vehicle and a driving action of the neighboring vehicle in the nearest disappearing lane (904); detect a license plate of the neighboring vehicle (906); receive a first signal from the neighboring vehicle for a driving mode (908); detect a point of lane disappearance in the nearest disappearing lane (910); determine a time interval for the neighboring vehicle to reach to the point of lane disappearance (912); execute a risk assessment of a collision based on the driving action (914) and then transmit a control command to an actuator system (318) of a host vehicle based on the risk assessment (916).