Vehicle Routing System Minimizing Sensor Cleaning Disruptions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous and semi-autonomous vehicles face disruptions in continuous travel due to frequent sensor cleaning requirements, which are triggered by varying road conditions and sensor maintenance needs, leading to temporary loss of autonomous operation and passenger discomfort.
Innovation Solution
A routing system that includes sensors, cleaning fluid reservoirs, and processors to collect and analyze usage data for determining optimal routes based on sensor maintenance conditions, selecting routes that minimize cleaning fluid consumption and disruptions, thereby extending travel distance and maintaining continuous autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle frequently cleans sensors to maintain detection accuracy, then sensor maintenance effectiveness is improved, but trip continuity is disrupted requiring manual intervention
Solution Approach 1:
The routing system proactively identifies and avoids sensor-dirtying segments before the vehicle reaches them, using historical usage data to predict cleaning requirements. This preliminary route optimization prevents the need for frequent cleaning interruptions during the trip, maintaining both sensor accuracy and trip continuity.
Solution Approach 2:
The system uses historical cleaning data to automatically learn and adapt to sensor maintenance patterns for different road segments, enabling the routing algorithm to self-optimize routes that minimize cleaning interruptions without requiring manual intervention or external input.
2Length of moving object
If the vehicle takes routes with more sensor-dirtying segments, then travel distance is reduced, but cleaning fluid consumption increases
Solution Approach 1:
The routing system changes the optimization parameter from purely distance-based to a composite metric that incorporates cleaning fluid consumption predictions. By weighting route selection based on historical usage data and predicted cleaning requirements, the system identifies routes that balance travel distance with fluid conservation.
Solution Approach 2:
The system continuously learns from actual cleaning fluid usage on different route segments, using this feedback to refine future routing decisions. Historical usage data is accumulated and applied to predict fluid consumption for upcoming segments, enabling dynamic route optimization that reduces overall fluid consumption.
3Reliability
If the vehicle requires manual control during cleaning, then sensor cleaning effectiveness is improved, but productivity is reduced due to interruptions
Solution Approach 1:
The routing system proactively routes the vehicle around segments known to cause heavy sensor contamination, using historical data to predict cleaning events. This preliminary avoidance prevents cleaning interruptions before they occur, maintaining productivity while ensuring sensor effectiveness through targeted routing rather than frequent cleaning cycles.
Solution Approach 2:
The system optimizes routes to minimize interruptions to the primary useful action of traveling. By selecting routes with lower predicted cleaning requirements based on historical usage data, the system maintains continuous autonomous operation and productive travel while still ensuring sensors remain effective through strategic route selection.
Data Source
AI summary
System, methods, and other embodiments described herein relate to selecting a route for a vehicle to travel. In one embodiment, the routing system determines a travel route for a vehicle by obtaining usage data for one or more segments of travel, the usage data indicating at least an amount of sensor cleaning fluid used during travel of the vehicle along the one or more segments. The routing system determines a plurality of routes to a destination, and selects a route from among the plurality of routes, the route being selected based at least in part on the usage data.