Vehicle Path Planning Under Adverse Weather Constraints
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Solution Overview
Problem
Current ADAS and ADS systems fail to account for adverse weather conditions, leading to non-ideal path planning and remote vehicle trajectory predictions, and do not consider unconventional maneuvers necessary to avoid collisions.
Innovation Solution
A method and system for path planning that determines a predicted trajectory of a remote vehicle, evaluates multiple possible trajectories based on weather conditions, and selects an optimal trajectory using evaluation metrics such as collision potential, maneuverability, and traffic violation scores, adjusting the vehicle's operation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current ADAS and ADS systems perform path planning without considering weather conditions, then the system complexity remains low, but the reliability of path planning deteriorates in adverse weather
Solution Approach 1:
The system dynamically adjusts path planning parameters based on weather conditions. When adverse weather is detected, the system modifies evaluation metrics, uncertainty biases, and trajectory selection criteria to account for reduced traction, visibility, and vehicle control capabilities, thereby maintaining reliability without permanent system complexity
Solution Approach 2:
The path planning system transitions from a static to a dynamic architecture where weather conditions trigger adaptive changes in evaluation metrics and trajectory generation. The system activates weather-specific algorithms only when needed, maintaining simplicity in normal conditions while enhancing reliability during adverse weather events
2Reliability
If the system evaluates multiple trajectories with weather-based metrics, then the collision risk decreases, but the computational time increases
Solution Approach 1:
The system evaluates multiple trajectories simultaneously but applies full weather-based metric analysis only to the most promising candidates. Less critical trajectories receive simplified evaluation, reducing computational overhead while maintaining adequate collision avoidance through selective detailed analysis of high-priority paths
Solution Approach 2:
The system pre-calculates weather impact factors and uncertainty biases before trajectory evaluation begins. By preparing weather-specific adjustment parameters in advance, the system reduces real-time computational burden during actual path selection while maintaining thorough collision avoidance analysis
3Measurement precision
If the system applies bias to uncertainties based on weather conditions, then the measurement precision of trajectory prediction improves, but the device complexity increases
Solution Approach 1:
The system applies weather-based bias adjustments locally to specific uncertainty components rather than uniformly to all predictions. Different uncertainty sources (e.g., position, velocity, trajectory) receive tailored bias corrections based on their sensitivity to weather conditions, improving precision without requiring complex global remodeling of all uncertainty sources
Data Source
AI summary
According to several aspects, a method for path planning for a vehicle includes determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of predicted trajectory nodes. The method further includes determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of possible trajectory nodes. The method further includes determining one or more evaluation metrics of each of the plurality of possible trajectory nodes based at least in part on a weather condition in an environment surrounding the vehicle. The method further includes selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of possible trajectory nodes. The method further includes performing a first action based at least in part on the optimal trajectory.