Vehicle Trajectory Arbitration Using Predictive Risk Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional vehicle control systems struggle to accurately predict changes in vehicle speed and acceleration, leading to deviations in travel risk assessments and compromised safety and ride comfort during automatic driving.
Innovation Solution
A vehicle control device that includes a travel profile information generation unit, a three-dimensional object behavior prediction unit, a risk map generation unit, a driving planning unit, and a trajectory arbitration unit, which collectively predict the behavior of three-dimensional objects around the vehicle and generate risk maps to prioritize safe trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the existence time range of the host vehicle is decided based on current vehicle speed and acceleration, then the risk map can be generated, but the risk level greatly deviates from the actual traveling state when future changes are large
Solution Approach 1:
The system performs preliminary actions by predicting multiple future travel profiles (acceleration, deceleration, constant speed) before generating the risk map. This allows the risk assessment to account for anticipated changes in vehicle state, thereby improving accuracy and reliability of safety evaluation.
Solution Approach 2:
The system dynamically adjusts the risk map generation by creating multiple travel profile information sets corresponding to different future vehicle states (acceleration, deceleration, constant speed). This dynamic approach ensures the risk assessment adapts to various potential traveling conditions, resolving the contradiction between measurement precision and reliability.
2Measurement precision
If multiple target behavior candidates are considered, then the trajectory selection becomes more accurate, but the calculation complexity increases
Solution Approach 1:
The system segments the trajectory selection process by evaluating multiple target behavior candidates (lane keeping, lane changing, obstacle avoidance) separately. Each candidate generates its own travel profile information and risk map, allowing systematic comparison and selection without overwhelming computational burden.
Solution Approach 2:
The system applies partial action by focusing calculations on the most relevant target behavior candidates rather than exhaustively analyzing all possible trajectories. The driving planning unit calculates priority values for each candidate and selects the most promising ones, reducing overall calculation complexity while maintaining accuracy.
Data Source
AI summary
A vehicle control device includes: a travel profile information generation unit that indicates a travel state of a host vehicle; a three-dimensional object behavior prediction unit that predicts an behavior of a three-dimensional object; a risk map generation unit that generates a risk map indicating a travel safety degree of the host vehicle on the basis of a prediction result of the behavior of the three-dimensional object and the travel profile information; a driving planning unit that calculates a priority indicating a degree to which the host vehicle is to preferentially make a selection; and a trajectory arbitration unit that selects a target trajectory of the host vehicle on the basis of the risk map and the priority.