Cloud-Based Speed-Limit Heuristic Tuning via Reinforcement Learning
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Solution Overview
Problem
Current road-vehicle speed-limit information systems face challenges in accurately determining current speed limits due to conflicting information from various sources, requiring labor-intensive tuning and costly fine-tuning processes, and are not optimized for diverse driving environments.
Innovation Solution
Implementing online learning or reinforcement learning methods to continuously improve decision heuristics, utilizing a cloud service to aggregate and distribute parameter sets, and clustering vehicles by driving environments to optimize speed-limit determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple information sources (cameras, geospatial databases) are combined to determine speed-limit, then the system can handle diverse driving situations, but the sources may supply contradictory information requiring complex decision heuristics
Solution Approach 1:
The patent introduces cloud-based information sources and standardized data formats as intermediaries between multiple information sources and the decision unit. The cloud service aggregates data from various sources (cameras, geospatial databases, traffic management systems) and presents unified, non-contradictory information, eliminating the need for complex conflict resolution heuristics while maintaining adaptability to diverse driving situations
Solution Approach 2:
The patent merges multiple information sources and their processing functions into a unified cloud-based system. Instead of each vehicle independently combining data from cameras, geospatial databases, and other sources with complex local heuristics, the cloud service consolidates these sources and provides integrated speed-limit information, reducing local computational complexity while improving adaptability
2Measurement precision
If conditional heuristics are fine-tuned by engineers to improve speed-limit selection accuracy, then system performance improves, but labor-intensive testing and verification are required
Solution Approach 1:
The patent implements self-service through automated machine learning and reinforcement learning systems that continuously optimize speed-limit selection algorithms without human intervention. The system automatically learns from real-world driving data, performs A/B testing of different heuristics, and deploys optimized parameters, eliminating the need for manual engineering tuning and verification while maintaining high accuracy
Solution Approach 2:
The patent establishes feedback loops where the system continuously monitors the performance of speed-limit selection heuristics in real-world conditions and automatically adjusts parameters based on measured outcomes. This closed-loop feedback system enables continuous improvement of accuracy without requiring repeated manual tuning cycles, significantly reducing the time investment required
3Reliability
If online learning or reinforcement learning is used to continuously fit heuristic parameters to reproduce cloud service estimated true speed-limits, then confidence in speed-limit values increases, but computational resources are required
Solution Approach 1:
The patent segments the computational workload by performing intensive online learning and reinforcement learning computations in the cloud rather than in-vehicle. The cloud service executes the computationally demanding parameter fitting algorithms to reproduce estimated true speed-limits, while vehicles only receive the pre-computed results, significantly reducing in-vehicle energy consumption while maintaining high confidence in speed-limit values
Solution Approach 2:
The patent uses copying by having the cloud service pre-compute and store optimized heuristic parameters based on aggregated data from multiple vehicles. Individual vehicles copy these pre-computed parameters rather than independently performing computationally intensive learning, reducing energy consumption while achieving the same reliability and confidence in speed-limit determinations
Data Source
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AI summary
Described herein is a method of determining a current location speed-limit in a road-vehicle (1) speed-limit information system (2). One or more signals (3, 4) corresponding to respective candidate speed-limits for the current location are received. A parametrized heuristic algorithm with an associated cost function is applied (7) to decide which candidate speed-limit (3, 4), if any, that is applicable. If available for the current location, a cloud service (8) supplied estimated true speed-limit (9) and an associated confidence in this estimate is received. An online learning or a reinforcement learning method is used to, based on the cloud service (8) supplied estimated true speed-limit (9) and the associated confidence in that estimate, constantly fit (10) the heuristic's parametrization to, with a high confidence, reproduce the cloud service (8) supplied estimated true speed-limit (9). A speed-limit information signal (11) corresponding to the decision of the parametrized heuristic algorithm is output.