Autonomous Vehicle Route Estimation for Preferred Drop-Off Points
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Solution Overview
Problem
Passengers often enter suboptimal addresses for their final destinations, leading to misalignment in ride characteristics and navigation expectations, which can impact factors like time, distance, and cost in autonomous vehicle services.
Innovation Solution
The system determines alternative destinations and routes based on passenger preferences, using a neural network model to estimate optimal drop-off points that align with user preferences, such as minimizing walking distance or avoiding traffic, by analyzing user data and route history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses the address entered by the passenger as the destination, then the navigation reaches the specified location, but the route characteristics may not align with passenger expectations and true final destination
Solution Approach 1:
The system performs preliminary analysis of the entered address to determine if it represents a true final destination or merely an intermediate location. By proactively analyzing address characteristics and comparing against known destination patterns, the system identifies whether additional navigation to the actual destination is needed before completing the ride
Solution Approach 2:
The system incorporates feedback loops that continuously assess whether the entered address aligns with the passenger's true destination. Based on this feedback, the system dynamically adjusts the navigation target, offering corrections or alternative destinations that better match passenger expectations and requirements
2Adaptability or versatility
If the system provides multiple alternative destinations and routes, then passenger preference alignment improves, but system complexity increases
Solution Approach 1:
The system applies different processing levels to different destinations based on their characteristics. For each potential destination, the system evaluates specific local qualities such as accessibility, relevance to the entered address, and passenger preference matching, rather than applying uniform complex processing to all possible destinations
Solution Approach 2:
The system dynamically adjusts parameters such as the number of alternative destinations presented, the depth of analysis for each destination, and the criteria for destination selection based on real-time conditions and passenger behavior patterns, optimizing the balance between adaptability and complexity
Data Source
AI summary
Disclosed is a method including obtaining, using at least one processor, a user destination; obtaining, using the at least one processor, preference data indicative of a user route preference; determining, using the at least one processor, based on the user destination and the preference data, a set of routes towards a set of destinations, wherein at least one route of the set of routes is associated with one or more operational metrics; ranking, using the at least one processor, based on the preference data, the routes of the set of routes; and controlling, using the at least one processor, based on at least one of the ranked routes, navigation of an autonomous vehicle Systems and computer program products are also provided.