Drop-Off Point Mapping Using Vehicle Disembarkation Patterns
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Solution Overview
Problem
Route planning systems often lack information on drop-off points, leading to suboptimal routes, missed stops, extended travel durations, and unpredictable delivery or arrival times, as well as inefficient workload distribution among drivers and resources.
Innovation Solution
A system and method for generating drop-off points in a map using sensors to detect vehicle location, disembarkation, driving patterns, and geometric features, determining a drop-off probability, and labeling locations as drop-off points if the probability exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If route planning is performed without drop-off point information, then the route planning system is simple, but the travel duration increases and delivery times become unpredictable
Solution Approach 1:
The system performs preliminary identification and labeling of drop-off points in advance by analyzing historical vehicle data, geometric features, and disembarkation patterns. This pre-processing of location information enables route planning systems to access ready-made drop-off point data, eliminating the need for real-time discovery and reducing overall travel duration.
Solution Approach 2:
The invention transitions from traditional two-dimensional map data to a three-dimensional representation by incorporating vertical clearance information, geometric features, and multi-layer spatial data. This dimensional enrichment allows the system to identify suitable drop-off points that consider not only horizontal location but also vertical accessibility and spatial constraints.
2Measurement precision
If multiple sensors and data processing are used to identify drop-off points, then delivery time accuracy improves, but the device complexity increases
Solution Approach 1:
The system employs multi-functional sensors that serve multiple purposes: GPS receivers provide both location tracking and drop-off point identification, cameras capture both vehicle surroundings and geometric features, and proximity sensors detect both obstacles and suitable drop-off locations. This multi-functionality reduces the need for dedicated specialized sensors, balancing measurement precision with device complexity.
Solution Approach 2:
The system utilizes existing vehicle data infrastructure and historical operational data to self-generate drop-off point information. By leveraging already-collected vehicle location, speed, and route data, the system reduces the need for additional complex sensing equipment while maintaining accurate delivery time predictions through data-driven insights.
3Productivity
If drop-off points are identified using geometric features and driving patterns, then route optimization improves, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary analysis of driving patterns, stopping behaviors, and geometric features to pre-identify drop-off points before route planning occurs. This advance processing transforms raw sensor data into structured drop-off point information, reducing the computational burden during actual route optimization and improving overall productivity.
Solution Approach 2:
The system creates simplified representations or models of complex driving patterns and geometric features. By generating abstracted drop-off point data that captures essential characteristics without retaining all raw sensor details, the system reduces data processing complexity while maintaining the ability to optimize routes effectively.
Data Source
AI summary
A system and method for generating drop-off points in a map includes a location sensor configured to detect a location of a vehicle, a proximity sensor configured to detect a disembarkation of a user from the vehicle and collect geometric features around the location, one or more sensors configured to monitor driving and stopping patterns of the vehicle at the location, and a processor. The processor is configured to generate a drop-off probability of the location based on the disembarkation of the user from the vehicle, the driving and stopping patterns of the vehicle, and the geometric features around the location, determine whether the drop-off probability is greater than a drop-off threshold, and in response to a determination that the drop-off probability is greater than the drop-off threshold, label the location as a drop-off point in the map.