Autonomous Vehicle Trajectory Planning with Component Latency Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately determining driving trajectories due to inherent latencies in sensor data processing, communication, and computational delays, leading to inaccurate decision-making and trajectory planning.
Innovation Solution
A trajectory planning system that models and accounts for component-level latencies within the vehicle, utilizing neural networks to optimize driving paths in real-time by simulating multiple trajectories and determining optimal vehicle operating parameters, including position, velocity, and acceleration, while considering sensor and computational latencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If component-level latencies are modeled and accounted for in real-time trajectory planning, then trajectory planning accuracy and vehicle response time are improved, but computational complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary characterization of component latencies during system setup or calibration phases, storing latency profiles for sensors, processors, and actuators. This pre-characterization allows the real-time trajectory planner to use predetermined latency values rather than measuring them during critical decision-making, reducing computational burden while maintaining accuracy.
Solution Approach 2:
The trajectory planning system is divided into separate modular components: a latency modeling module that characterizes individual component delays, a trajectory optimization module that calculates optimal paths, and a coordination module that synchronizes them. Each module handles specific computational tasks independently, making the overall system more manageable and efficient.
2Reliability
If multiple trajectories are simulated and evaluated in real-time, then decision-making accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system simulates and evaluates only a limited number of candidate trajectories (e.g., 3-5 most promising paths) rather than exhaustively evaluating all possible trajectories. The trajectory optimizer generates candidate paths based on heuristics and constraints, and the system selects from this reduced set, achieving sufficient decision-making accuracy without exhaustive computation.
Solution Approach 2:
Candidate trajectories are pre-generated using simplified models and heuristics before full evaluation. The system quickly filters out obviously suboptimal paths using coarse criteria, then performs detailed simulation only on the remaining candidate trajectories, reducing the number of full simulations required.
3Speed
If sensor data processing latency is reduced, then real-time responsiveness is improved, but data processing depth and accuracy may be compromised
Solution Approach 1:
Sensor data processing is divided into multiple passes: a quick initial pass that extracts critical information for immediate response, followed by more detailed processing that refines the data. The system uses the rapidly processed data for time-critical decisions while utilizing the more thoroughly processed data for longer-term planning and verification.
Solution Approach 2:
The system performs preliminary sensor data validation and filtering using simplified algorithms that can operate with minimal latency. Critical sensor readings are identified and prioritized for immediate processing, while less time-sensitive data undergoes more comprehensive analysis.
Data Source
AI summary
Systems and methods for autonomous driving which calculates path trajectories by incorporating latencies of vehicle components and calculations so that when the vehicle is in an autonomous driving mode and using a trajectory to calculate a designated path, the latencies of each vehicle component can be taken into account to provide a more accurate vehicle path.