Autonomous Vehicle Pickup Location Switching for Lower-Cost Routing
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Solution Overview
Problem
Autonomous vehicles face challenges in determining the most efficient side of a street for pickup or drop-off locations, as they lack human interaction to arrange exact locations, leading to inefficient routing and potential complications like U-turns or unprotected left turns.
Innovation Solution
The vehicle's computing devices calculate and compare costs to reach different sides of a street, including passenger crossing difficulties, to identify the lowest-cost route and send notifications to passengers for potential side changes, allowing for autonomous routing adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle strictly follows the initially assigned pickup location, then the routing instructions are simple and direct, but the vehicle may encounter inefficient routing, U-turns, or unprotected left turns
Solution Approach 1:
The system dynamically adjusts the pickup location based on real-time route analysis. The processor evaluates the initial route to the assigned pickup location and automatically modifies the location if inefficiencies are detected, transforming a static routing system into an adaptive one that optimizes for travel efficiency.
Solution Approach 2:
The autonomous vehicle system performs self-optimization of pickup locations without human intervention. The processor autonomously analyzes routing efficiency and adjusts the pickup location independently, enabling the system to serve itself by eliminating inefficient maneuvers like U-turns and unprotected left turns.
2Loss of time
If the autonomous vehicle changes the pickup location to optimize routing, then routing efficiency improves and complex maneuvers are avoided, but the vehicle deviates from the originally assigned location
Solution Approach 1:
The system implements a feedback mechanism where the processor continuously monitors the assigned pickup location against routing efficiency criteria. When inefficiencies are detected, the system provides feedback by automatically adjusting the pickup location to an alternative that eliminates wasteful maneuvers, thereby reducing wait time while maintaining acceptable location accuracy.
Solution Approach 2:
The system changes the spatial parameters of the pickup location based on routing analysis. The processor modifies the location coordinates to alternative positions that are equally accessible to passengers but eliminate inefficient vehicle maneuvers, optimizing the balance between location fidelity and routing efficiency.
3Productivity
If the autonomous vehicle calculates multiple route options and alternative pickup locations, then routing efficiency improves, but the computational complexity and processing time increase
Solution Approach 1:
The routing optimization process is segmented into distinct evaluation stages. The processor first analyzes the initial route for specific inefficiency patterns (U-turns, unprotected left turns), then selectively generates alternative pickup locations only when needed, rather than computing all possible routes simultaneously. This segmented approach reduces overall computational complexity while maintaining pickup efficiency.
Solution Approach 2:
The system performs partial route analysis by focusing specifically on detecting inefficient maneuvers rather than evaluating every possible route variant. This selective analysis approach provides sufficient optimization without the excessive computational burden of exhaustive route comparison, achieving practical pickup efficiency with manageable processing complexity.
Data Source
AI summary
Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a first location corresponding to a location where the vehicle is to pick up or drop off a passenger is received. A first cost for the vehicle to reach the first location is determined. A second location based on the first location is identified, and a second cost is determined based on a cost for the vehicle to reach the second location and a cost for the passenger to reach the second location. The first cost is compared to the second cost, and a notification is sent based on the notification. In response to sending the notification, instructions to proceed to the second location are received, and in response to receiving the instructions, the vehicle is controlled in the autonomous driving mode to the second location to pick up or drop off the passenger.