Vehicle Trajectory Optimization for Dynamic Road Obstacles
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Solution Overview
Problem
Traditional algorithms for route selection in vehicle navigation are impractical due to the complexity of large road networks and the presence of obstacles like parked vehicles and construction zones, leading to potential route overloading and navigation challenges.
Innovation Solution
The development of techniques to generate an optimal trajectory for vehicles by optimizing operational metrics such as safety, distance, and collision avoidance, using processors to rank and select travel segments that minimize costs and maximize safety, allowing for efficient navigation through complex road networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional algorithms are used for route selection in large road networks, then complete route coverage is achieved, but computational complexity becomes impractical
Solution Approach 1:
The patent segments the road network into a directed graph where intersections are nodes and road segments are edges. This segmentation transforms the complex problem of navigating entire road networks into manageable discrete units that can be processed efficiently by algorithms like A* or Dijkstra's, resolving the contradiction between complete coverage and computational complexity.
Solution Approach 2:
The patent introduces edge weights as an additional dimension to represent traversal costs (time, distance, traffic conditions). By adding this dimensional parameter, the system can optimize route selection not just by geometric coverage but by cost-based evaluation, achieving practical efficiency while maintaining route selection completeness through multi-criteria optimization.
2Speed
If greedy algorithms are used for route selection, then computational speed is improved, but route overloading occurs causing travel delays
Solution Approach 1:
The patent implements feedback mechanisms where edge weights are dynamically updated based on real-time traffic conditions, vehicle density, and historical data. This feedback loop allows the system to identify and redistribute traffic from overloaded routes to alternative paths, preventing route overloading while maintaining the computational efficiency of greedy algorithms through adaptive weight adjustment.
Solution Approach 2:
The system transitions from static route selection to dynamic routing where edge weights and route recommendations change in real-time based on current traffic conditions. This dynamic adaptation allows the system to respond to changing conditions, avoiding overloaded routes while maintaining computational speed through efficient weight update mechanisms.
3Reliability
If multiple operational metrics are optimized across travel segments, then navigation safety and efficiency are improved, but computational complexity increases
Solution Approach 1:
The patent transforms multiple operational metrics (safety, distance, time, fuel consumption) into a unified cost function through parameter weighting. By changing the representation of multiple metrics into a single composite edge weight, the system can optimize navigation across multiple criteria while maintaining computational tractability through standard shortest-path algorithms.
Solution Approach 2:
The patent merges multiple operational metrics into a unified trajectory optimization framework. By combining safety constraints, distance minimization, and other operational requirements into a single optimization objective, the system achieves multi-criteria optimization without the computational complexity of separate optimization processes.
Data Source
AI summary
Techniques for generation of an optimal trajectory for a vehicle include receiving, using one or more processors of the vehicle, an instruction for the vehicle to travel from an initial spatiotemporal location to a destination spatiotemporal location. One or more processors are used to generate a trajectory including travel segments. The trajectory begins at the initial spatiotemporal location and terminates at the destination spatiotemporal location. Each travel segment begins at a first spatiotemporal location and terminates at a second spatiotemporal location. Each travel segment is associated with operational metrics. The operational metrics are associated with navigating the vehicle from the first spatiotemporal location to the second spatiotemporal location. Each operational metric is optimized across the travel segments to generate the trajectory. Using a control module of the vehicle, the vehicle is navigated from the initial spatiotemporal location to the destination spatiotemporal location along the trajectory.