Autonomous Vehicle Rider Entry Time Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in determining safe and efficient pick-up and drop-off locations due to reduced interaction with passengers, leading to potential congestion and traffic issues, as they lack the ability to communicate effectively like human drivers.
Innovation Solution
The autonomous vehicle uses a system that includes sensor systems, an internal computing system, and a remote computing system to determine and classify potential pick-up and drop-off locations based on real-time data, calculating the time required for passenger entry and exit, and classifying zones as relaxed, normal, or rushed to optimize location selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles stop to pick up and drop off riders, then passenger service is provided, but the vehicle becomes a hazard and causes traffic congestion
Solution Approach 1:
The system performs preliminary classification of zones (relaxed, normal, rushed) and preliminary calculation of rider entry/exit times before the vehicle stops. This advance preparation allows the vehicle to select optimal stop locations and timing, reducing unnecessary delays and minimizing traffic congestion while ensuring safe passenger service.
Solution Approach 2:
The system dynamically adjusts operational parameters based on real-time conditions. It changes the vehicle's stopping behavior by selecting different zone types (relaxed, normal, rushed) based on traffic conditions, rider preferences, and calculated entry/exit times. This parameter adjustment optimizes the balance between passenger service quality and traffic flow maintenance.
2Reliability
If the vehicle stops for extended periods to ensure passenger safety, then passenger safety is improved, but traffic congestion increases
Solution Approach 1:
The system dynamically adjusts stopping duration based on multiple parameters including zone classification, rider preferences, and real-time traffic conditions. By changing these parameters adaptively, the vehicle ensures sufficient time for safe passenger entry and exit while minimizing unnecessary delays that would congest traffic.
Solution Approach 2:
The system makes the stopping behavior dynamic rather than static. It continuously monitors traffic conditions, rider preferences, and zone characteristics to adjust stopping timing and duration in real-time. This dynamic approach ensures passenger safety requirements are met while adapting to traffic flow conditions to minimize congestion.
3Adaptability or versatility
If the vehicle communicates with passengers to agree on pick-up/drop-off locations, then location suitability is optimized, but the autonomous vehicle lacks this communication capability
Solution Approach 1:
The autonomous vehicle performs location selection autonomously without requiring direct communication with passengers. The system classifies zones, calculates rider entry/exit times, and selects optimal stop locations based on pre-configured rider preferences and real-time sensor data. This self-service approach achieves adaptability in location selection while avoiding the complexity of real-time passenger communication systems.
Solution Approach 2:
The system uses rider preference profiles as an intermediary between the vehicle's autonomous decision-making and passenger needs. Instead of direct communication, the vehicle references pre-stored rider preferences to make adaptive location selections, effectively mediating the interaction without requiring complex real-time communication capabilities.
4Measurement precision
If the vehicle uses complex sensor systems and computing systems to determine optimal locations, then location selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the environment into distinct zone classifications (relaxed, normal, rushed) based on traffic conditions and characteristics. This segmentation simplifies the complex task of location selection by breaking it into manageable categories, allowing the vehicle to make accurate decisions through systematic evaluation of predefined zone types rather than analyzing every possible variable.
Data Source
AI summary
An autonomous vehicle having observational sensors and a computing system. The computing system may have a processor and memory having computer-executable instructions that may cause the processor to determine, based upon a profile of a user, an average amount of time it takes the user to enter or exit the autonomous vehicle. The processor may then cause the observational sensors to detect and observe obstacles around the locations. The processor may also determined, based upon detected objects around the autonomous vehicle and the average amount of time it takes the user to enter or exit the autonomous vehicle, a location to stop to allow the user to enter or exit the autonomous vehicle.