Vehicle Collision Sandbox Using Object-Slice Trajectory Simulation
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Solution Overview
Problem
Current technologies face challenges in enabling semi-autonomous or fully autonomous vehicles to safely navigate their surroundings by effectively identifying collision probabilities with surrounding objects, which is a complex and technically demanding task.
Innovation Solution
The implementation of an intelligent vehicle-collision simulation sandbox that generates sequences of single-object slices for surrounding objects based on their initial slices, calculates collision probabilities, and displays representative displacement trajectories to users, using deep learning and machine learning neural networks to simulate potential collisions and enhance safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional collision detection methods are used, then the system is simpler to implement, but the collision probability identification accuracy is insufficient
Solution Approach 1:
The patent creates virtual copies of the current vehicle and surrounding objects in a sandbox environment to simulate collision scenarios. Instead of directly analyzing real-world data, the system generates synthetic collision data through virtual replicas, enabling accurate collision probability identification without requiring complex real-time sensing and analysis infrastructure.
Solution Approach 2:
The system performs preliminary collision simulations in advance by generating multiple sequences of single-object slices that represent probable collision trajectories. By pre-calculating collision probabilities through virtual simulations before actual navigation decisions, the system achieves high accuracy without needing complex real-time computation during vehicle operation.
2Measurement precision
If multiple sequences of single-object slices are generated for collision simulation, then the collision probability calculation becomes more accurate, but the computational time and processing complexity increase
Solution Approach 1:
The patent generates N sequences of single-object slices (where N is a predetermined number) to achieve sufficient collision probability accuracy without exhaustively simulating all possible trajectories. By limiting the simulation to a reasonable number of sequences rather than all potential paths, the system balances computational efficiency with accurate collision risk assessment.
Solution Approach 2:
The system divides the collision simulation process into discrete single-object slices that represent sequential positions of surrounding objects along probable trajectories. By segmenting the continuous motion into discrete slices and processing them in sequence, the system efficiently calculates collision probabilities without requiring complex continuous mathematical modeling.
3Reliability
If the system analyzes behaviors of all surrounding objects in real-time, then the collision detection coverage is comprehensive, but the processing speed and response time decrease
Solution Approach 1:
The patent extracts only the relevant surrounding objects and their key behavioral characteristics needed for collision analysis, rather than processing all environmental data. By isolating and analyzing only the objects within the sandbox region that pose potential collision risks, the system achieves comprehensive collision detection coverage while maintaining high processing speed through selective data extraction.
Data Source
AI summary
Embodiments of the present disclosure provide systems and methods for implementing an intelligent vehicle-collision simulation sandbox and intelligently analyzing behaviors of surrounding objects with a current vehicle to simulate possible collisions of the current vehicle and surrounding objects and display intelligently calculated collision probabilities of the surrounding objects.