AI Collision Avoidance With Harm-Minimizing Vehicle Control
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Solution Overview
Problem
Current collision-avoidance systems fail to effectively recognize traffic hazards, particularly in-lane and side-encroachment hazards, and often result in increased damage and injuries due to inadequate reaction time and lack of user-adjustable features, as well as failing to minimize harm when collisions are unavoidable.
Innovation Solution
A system utilizing artificial intelligence (AI) models to analyze sensor data from vehicles, determining whether collisions are avoidable or unavoidable, and implementing sequences of actions such as braking, acceleration, or steering to either avoid collisions or minimize their impact, while also accounting for driver abilities and adjusting interventions based on real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current collision-avoidance systems apply brakes rapidly to avoid collision, then collision avoidance is improved, but vehicle skidding and loss of control occurs
Solution Approach 1:
The system dynamically adjusts braking intensity based on real-time analysis of multiple factors including vehicle speed, road conditions, weather, and following vehicle distance. Rather than applying maximum braking force immediately, the system modulates brake pressure to achieve collision avoidance while maintaining vehicle stability and preventing skidding.
Solution Approach 2:
The system changes multiple parameters simultaneously including brake force magnitude, steering angle adjustments, and acceleration modifications to achieve collision avoidance. By varying these parameters in combination rather than relying solely on rapid braking, the system prevents harmful effects like skidding while maintaining effectiveness.
2Reliability
If collision-avoidance systems intervene aggressively to prevent collisions, then collision avoidance is improved, but damage and injuries increase due to skidding and loss of control
Solution Approach 1:
The system employs a multi-parameter control strategy that adjusts braking force, steering angle, and acceleration in a coordinated manner. This approach achieves collision avoidance while distributing the intervention across multiple controlled parameters, preventing the excessive single-action interventions that cause skidding and associated damage.
Solution Approach 2:
The system continuously monitors vehicle response to intervention actions and adjusts subsequent actions based on real-time feedback. If skidding or loss of control begins to occur, the system detects these conditions and modifies its intervention to prevent harmful effects, thereby reducing damage and injuries.
3Device complexity
If collision-avoidance systems use fixed intervention strategies, then system complexity is reduced, but adaptability to different driver abilities and conditions deteriorates
Solution Approach 1:
The system transitions from fixed intervention strategies to dynamic, adaptive strategies that respond to real-time conditions and driver characteristics. By continuously adjusting intervention parameters based on driver ability assessments and environmental conditions, the system achieves high adaptability without requiring excessive complexity in the base system architecture.
4Reliability
If AI models analyze multiple sequences of actions to determine optimal collision response, then collision mitigation effectiveness is improved, but computing time and processing requirements increase
Solution Approach 1:
The system pre-calculates and stores multiple sequences of collision-mitigation actions before they are needed. When a collision risk is detected, the AI model selects from these pre-prepared sequences rather than generating them in real-time, significantly reducing computing time while maintaining the ability to evaluate multiple action sequences for optimal effectiveness.
Data Source
AI summary
In a traffic emergency, there is no time for a human to integrate multiple sensor data streams and devise a plan for avoiding a collision. Only the electronic reflexes of a trained automatic system can provide evasive action in time. Disclosed is an artificial intelligence (AI) model trained to recognize an imminent collision based on sensor data, rapidly devise and test a large number of possible sequences of actions, some drawn from a library of previously-successful strategies and others invented by the AI model. If any sequence can avoid the collision, the AI model implements that sequence immediately. If none of the sequences can avoid the collision, the AI model calculates the harm caused by each sequence and picks the one that causes the least harm (fatalities, injuries, etc.) for implementation. AI is needed to find a possible solution in time to implement it and thereby mitigate the imminent collision.