Autonomous Vehicle Pickup Routing and Traffic Impact Control
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Solution Overview
Problem
Autonomous vehicles face challenges in determining optimal waiting patterns when arriving to pick up users, as they must balance traffic impact and user arrival time, often leading to inefficiencies and potential traffic congestion.
Innovation Solution
The system uses a computing system to assess traffic data and user arrival information to decide whether to stop in a travel lane or re-route, considering factors like traffic thresholds and user arrival estimates to minimize disruption and optimize waiting strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the autonomous vehicle stops in a travel lane to wait for the user, then the user pickup service is provided, but traffic congestion increases
Solution Approach 1:
The patent introduces a computing system as an intermediary that coordinates between the autonomous vehicle, user devices, and traffic management. This system enables the vehicle to communicate with users about arrival times and coordinates waiting actions, reducing unnecessary stops and traffic disruption while maintaining reliable user pickup service
Solution Approach 2:
The system dynamically adjusts vehicle waiting behavior based on real-time conditions including user arrival predictions, traffic patterns, and location characteristics. The autonomous vehicle can adapt its stopping duration and location dynamically rather than following fixed waiting patterns, optimizing the balance between user service and traffic flow
2Object-generated harmful factors
If the autonomous vehicle travels along a holding pattern route, then traffic impact is reduced, but user arrival time accuracy decreases
Solution Approach 1:
The system implements feedback loops where the computing system continuously monitors user device location data, predicts arrival times, and communicates updated estimates to users. This feedback mechanism maintains arrival time accuracy even when vehicles follow holding pattern routes, as users are informed of predicted arrival times based on their actual approach speed and distance
Solution Approach 2:
The system performs preliminary actions by notifying users of approaching vehicle arrival before the vehicle actually stops. Users receive advance notice and can prepare for pickup, reducing the perceived waiting time and maintaining accuracy expectations even when the vehicle follows optimized holding patterns rather than direct routing
3Ease of operation
If the autonomous vehicle stops in a travel way, then user pickup is facilitated, but traffic flow is disrupted
Solution Approach 1:
The system applies different waiting strategies based on local characteristics of the pickup location. Urban locations with high pedestrian activity receive different treatment than suburban locations, and locations near intersections differ from those on open roads. This localized approach optimizes user pickup facilitation while minimizing traffic disruption specific to each location's context
Solution Approach 2:
The system changes operational parameters such as stopping duration, stopping location offset from curb, and engine idle state based on traffic conditions and user characteristics. These parameter adjustments allow the vehicle to facilitate user pickup while adapting to local traffic flow requirements, reducing overall traffic disruption
4Object-generated harmful factors
If the autonomous vehicle uses holding patterns, then traffic congestion is minimized, but system complexity increases
Solution Approach 1:
The computing system performs multiple functions including traffic coordination, user communication, arrival prediction, and vehicle routing optimization. By consolidating these functions in a single multi-functional system rather than separate dedicated systems for each function, the overall system complexity is managed while achieving comprehensive traffic optimization through holding patterns
Data Source
AI summary
Systems and methods for controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining data indicative of a location associated with a user to which an autonomous vehicle is to travel. The autonomous vehicle is to travel along a first vehicle route that leads to the location. The method includes obtaining traffic data associated with a geographic area that includes the location. The method includes determining an estimated traffic impact of the autonomous vehicle on the geographic area based at least in part on the traffic data. The method includes determining vehicle action(s) based at least in part on the estimated traffic impact and causing the autonomous vehicle to perform the vehicle action(s) that include at least one of stopping the autonomous vehicle at least partially in a travel way within a vicinity of the location or travelling along a second vehicle route.