Autonomous Vehicle Collision Avoidance on Icy Uphill Roads
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating icy uphill roads due to reduced traction, increased sliding risks, longer braking distances, and reduced visibility, which can lead to collisions.
Innovation Solution
A system comprising a processor, computer vision module, and control module that detects road surface conditions, adjusts safe distance, identifies target vehicles, and predicts collisions to initiate collision avoidance actions, such as altering safe distance and providing warnings or changing lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle maintains a standard safe distance on icy uphill roads, then the vehicle can operate with normal following distance, but the risk of collision increases due to reduced traction and longer braking distances
Solution Approach 1:
The system applies preliminary anti-action by detecting icy uphill road conditions and predicting potential collisions before they occur. The control module proactively adjusts the safe distance parameter and prepares collision avoidance actions, counteracting the harmful effects of reduced traction and extended braking distances before they lead to actual collisions.
Solution Approach 2:
The system performs preliminary action by continuously monitoring road surface conditions, target vehicle behavior, and speed changes to predict collisions in advance. The control module adjusts the safe distance parameter before collision risk materializes, enabling the autonomous vehicle to take preventive measures rather than reactive responses.
2Reliability
If the autonomous vehicle increases safe distance on icy uphill roads, then collision risk is reduced, but the vehicle's operational efficiency and productivity decrease
Solution Approach 1:
The system applies dynamics by making the safe distance parameter adaptive rather than static. The control module continuously adjusts the safe distance based on real-time detection of road surface conditions, target vehicle characteristics, and speed changes. This dynamic adjustment allows the vehicle to maintain optimal safety margins while minimizing the impact on driving efficiency and productivity.
Solution Approach 2:
The system implements parameter changes by modifying the safe distance parameter according to detected road conditions and target vehicle behavior. The control module adjusts this critical parameter dynamically, enabling the vehicle to operate safely on icy uphill roads while maintaining reasonable productivity levels through optimized following distance.
3Measurement precision
If the autonomous vehicle detects and responds to target vehicle slowing down, then collision prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The system applies universality by designing the detection and control module to perform multiple functions: detecting road surface conditions, monitoring target vehicle speed and behavior, predicting collisions, and executing collision avoidance actions. This multi-functional approach improves collision prediction accuracy while avoiding the need for separate specialized systems for each function.
Solution Approach 2:
The system implements feedback by continuously monitoring target vehicle speed changes and using this information to update collision predictions. The control module receives feedback from the detection system about target vehicle behavior and adjusts its predictions and avoidance actions accordingly, improving accuracy through continuous information loops.
Data Source
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AI summary
An embodiment related to a system (340), wherein the system (340) is operable to determine a road surface condition, wherein the road surface condition is at least one of an ice, wet, and snow (302); adjust, a safe distance value of the host vehicle based on the road surface condition (304); detect a vehicle type, a speed, and a visible roof area of a target vehicle (306); determine an uphill road that the host vehicle is approaching (308); determine that the target vehicle is slowing down based on a change in speed of the target vehicle in real-time (310); and determine, a collision avoidance action for the host vehicle to avoid a collision with the target vehicle (312).