Autonomous Collision Escape Path Planning for Multi-Object Traffic
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Solution Overview
Problem
Current collision avoidance systems in vehicles are unable to effectively monitor multiple objects and adjust the vehicle's path based on the behavior of these objects, limiting their ability to prevent accidents.
Innovation Solution
The system predicts the behavior of moving objects using machine learning models and Monte Carlo Tree Search algorithms to calculate collision probabilities and determine the safest path for the vehicle, incorporating data from various sensors such as LIDAR, RADAR, and cameras to assess the vehicle's surroundings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current collision avoidance systems automatically brake when an object is detected, then the vehicle can avoid simple collisions, but the system cannot monitor numerous objects and adjust the vehicle's path based on object behavior
Solution Approach 1:
The system segments the monitoring task by dividing the environment into multiple zones (front, rear, left, right) and assigning different sensor types to different zones. Multiple objects are tracked independently with individual probability calculations, allowing the system to handle numerous objects without overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by continuously predicting object behaviors and calculating collision probabilities before actual collisions occur. Monte Carlo Tree Search algorithms pre-compute multiple possible future scenarios and their associated collision risks, enabling proactive path adjustment rather than reactive braking.
2Measurement precision
If the system uses machine learning models and Monte Carlo Tree Search to predict object behavior and calculate collision probabilities, then collision avoidance accuracy improves, but computational complexity increases
Solution Approach 1:
The system applies partial action by focusing computational resources on calculating collision probabilities for objects that pose actual threats. Not all detected objects require full Monte Carlo Tree Search analysis - only those within critical zones and exhibiting dangerous behavior patterns receive intensive computational attention.
Solution Approach 2:
The system uses machine learning models trained on historical data to create simplified copies of complex collision scenarios. These trained models can quickly evaluate new situations without performing full Monte Carlo simulations, reducing real-time computational complexity while maintaining accuracy.
3Reliability
If the vehicle autonomously determines the safest path based on real-time data analysis, then collision avoidance capability improves, but response time may be affected
Solution Approach 1:
The system implements periodic action by updating collision probability calculations and path determinations at fixed time intervals rather than continuously. This periodic refresh approach maintains reliable collision avoidance while preventing excessive computational overhead that would consume unnecessary processing time.
Solution Approach 2:
The system performs preliminary path planning by pre-calculating multiple possible escape routes and their associated collision probabilities. When a threat is detected, the vehicle can quickly switch to a pre-evaluated safe path rather than computing a new path from scratch, reducing response time while maintaining reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the vehicle to autonomously avoid collisions by determining the safest path based on real-time data analysis, enhancing safety and reducing the risk of accidents in complex driving environments.
Implementation Method 1
incorporating data from various sensors such as LIDAR, RADAR, and cameras to assess the vehicle's surroundings
Implementation Method 2
incorporating data from various sensors such as LIDAR, RADAR, and cameras to assess the vehicle's surroundings
Data Source
AI summary
Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.