Automated Vehicle Trajectory Planning With Quality-Based Braking
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Solution Overview
Problem
Existing methods for planning target trajectories for automated driving vehicles do not adequately consider the quality of object detection and adjust braking interventions accordingly, leading to potential excessive or inadequate braking responses, especially in complex traffic scenarios with multiple objects.
Innovation Solution
A method that assigns trajectory costs based on object detection quality, determines a minimum permissible acceleration for each object, and categorizes trajectories into filtered and unfiltered categories to ensure braking interventions align with detection reliability, thereby selecting a target trajectory that minimizes impact from low-quality detections while maintaining safety and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection is used to determine trajectory costs, then trajectory planning can avoid detected objects, but low-quality detections may trigger excessive braking interventions
Solution Approach 1:
The patent applies local quality by differentiating the treatment of detected objects based on their detection quality. High-quality detections trigger standard avoidance behavior with appropriate braking, while low-quality detections are treated differently to avoid excessive braking. This is implemented through quality-based cost assignment where the same object type receives different trajectory costs depending on detection quality metrics.
Solution Approach 2:
The patent changes the parameter of trajectory cost assignment based on detection quality. Instead of uniform cost assignment for all detected objects, the system dynamically adjusts trajectory costs according to detection quality parameters. This allows the trajectory optimization to account for uncertainty in detection reliability, preventing excessive braking responses to low-quality detections while maintaining appropriate responses to high-quality detections.
2Productivity
If multiple trajectory candidates are evaluated with object costs, then the vehicle can select optimal trajectories, but computational complexity increases
Solution Approach 1:
The patent segments the trajectory evaluation process by dividing trajectory candidates into filtered and unfiltered categories based on detection quality. This segmentation allows the system to apply different evaluation strategies: low-quality detections are filtered out or given minimal weight, while high-quality detections receive full evaluation. This reduces the effective search space and computational burden compared to evaluating all trajectory candidates with all detected objects equally.
Solution Approach 2:
The patent applies partial action by selectively applying full trajectory evaluation only to high-quality detections, while using simplified or filtered evaluation for low-quality detections. This partial evaluation approach reduces computational complexity by avoiding exhaustive evaluation of all trajectory candidates against all detected objects, particularly those with low detection quality that are less likely to represent real hazards.
3Reliability
If trajectory costs are assigned based on all detected objects, then comprehensive safety is achieved, but comfortable driving is reduced due to excessive braking
Solution Approach 1:
The patent performs preliminary filtering of detected objects based on detection quality before assigning trajectory costs. By pre-identifying and filtering out low-quality detections, the system prevents these unreliable detections from triggering unnecessary braking interventions. This preliminary action maintains safety by focusing on high-quality detections while eliminating the source of excessive braking that would compromise comfort.
Solution Approach 2:
The patent converts the potentially harmful effect of low-quality detections (which would cause excessive braking and discomfort) into a benefit by using detection quality as a filtering mechanism. The presence of low-quality detections is transformed from a source of problematic braking into an opportunity to demonstrate robust, comfortable driving behavior by appropriately downweighting or filtering these unreliable detections while maintaining response to genuine hazards.
Data Source
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AI summary
The invention relates to a method for planning a target trajectory for an automated vehicle (1), wherein for each object (2, 3) detected in a planning horizon (P), a quality value (Q) is determined, which indicates a measure of the reliability of the detection of the object (2, 3). For each object (2, 3), a minimum permissible acceleration (aQ), with which the vehicle (1) may be braked onto the object (2, 3), is determined as a function of the quality value (Q) of that object. Trajectory candidates, and the object costs determined for these, are evaluated as a function of an acceleration (ax(t)) specified by the respective trajectory candidate and as a function of a minimum permissible acceleration (aQ) during braking onto the object (2, 3). A target trajectory is selected as a function of trajectory costs from a set of trajectory candidates and additionally taking into account the evaluation of the trajectory candidates and the object costs determined for them.