Driver Assistance Black-Swan Detection With Dual Planning Horizons
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automotive driver assistance systems fail to effectively handle 'black swan events', which are unpredictable and rare events with catastrophic consequences, by neglecting low-probability but high-risk behaviors of other agents in the environment, leading to potential collisions.
Innovation Solution
A computer-implemented method for detecting black swan events by sensing the environment, predicting behaviors of other agents, computing probabilities and severities, and generating detection signals for a behavior planning system to switch to a short-term planner optimized for handling these events, using separate risk models and planning horizons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current driver assistance systems use standard risk models focusing on high-probability events, then computational efficiency is maintained, but black swan events with low probability and high severity are neglected leading to potential collisions
Solution Approach 1:
The planning system is divided into two separate modules: a long-term planner that handles normal high-probability scenarios, and a short-term planner that specifically handles black swan events. This segmentation allows each module to be optimized for its specific function, maintaining overall computational efficiency while improving reliability against rare events.
Solution Approach 2:
The system performs preliminary detection of black swan events by monitoring for unexpected low-probability situations before they escalate into collisions. By detecting these events early and switching to the short-term planner in advance, the system prepares appropriate responses before the actual collision risk materializes.
2Measurement precision
If the system monitors all possible behaviors of other agents with equal detail, then detection precision is improved, but computational resources are wasted on low-risk scenarios
Solution Approach 1:
The system applies different levels of monitoring detail to different scenarios: standard risk models are used for high-probability common scenarios, while enhanced detection algorithms are specifically applied to identify low-probability black swan events. This local differentiation of quality ensures precise detection where needed without wasting computational resources on routine scenarios.
Solution Approach 2:
The system dynamically changes detection parameters based on the situation. For black swan event detection, it uses specialized parameters focused on unexpected low-probability behaviors, while for normal operations it uses standard parameters. This parameter adaptation allows precise detection of critical events while maintaining computational efficiency for routine monitoring.
3Speed
If the system uses a single planning horizon for all scenarios, then device complexity is reduced, but responsiveness to emerging black swan events is delayed
Solution Approach 1:
The system dynamically adjusts the planning horizon based on the detected scenario type. For black swan events, it switches to a short-term planning horizon that provides rapid response to emerging threats. For normal high-probability scenarios, it uses a long-term planning horizon for efficient route optimization. This dynamic adaptation of planning horizons enables fast response to critical events while maintaining overall system efficiency.
Data Source
AI summary
A computer-implemented method for detecting black-swan events for assisting operation of an ego-agent comprises sensing an environment of the ego-agent that includes at least one other agent and predicting behaviors of the at least one other agent based on the sensed environment. The method includes detecting at least one potential black-swan event in the environment of the ego-agent by determining at least one predicted possible behavior of the at least one other agent for which a computed situation probability is smaller than a first threshold, a computed collision probability exceeds a second threshold, and a determined collision severity exceeds a third threshold.