Self-Driving Vehicle Feature Presentation Using Consumer Feedback
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Solution Overview
Problem
Self-driving vehicles (SDVs) face challenges in efficiently and automatically presenting their features to potential consumers, as existing methods lack the ability to analyze consumer preferences and contexts in real-time, leading to ineffective demonstrations of handling capabilities and features.
Innovation Solution
A computer-implemented system that determines SDV features based on consumer observations and contexts, generating instructions for the SDV to perform tasks that highlight appealing features, such as navigating to a consumer's location or demonstrating handling capabilities, using a cloud computing environment for data processing and task management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional feature presentation methods are used for self-driving vehicles, then the presentation process is simple, but the consumer engagement and interest are insufficient
Solution Approach 1:
The system continuously monitors consumer reactions (visual attention, audio responses, physiological signals) during the feature presentation and uses this feedback to dynamically adjust which features are demonstrated and how. This creates a closed-loop system that adapts the presentation in real-time based on consumer engagement levels, resolving the contradiction between personalized adaptation and system complexity by implementing feedback-driven automation.
Solution Approach 2:
The system autonomously performs the entire feature presentation process without requiring manual intervention from sales personnel. It independently analyzes consumer preferences, selects relevant features, controls vehicle demonstrations, and adjusts presentations based on real-time feedback. This self-service approach handles the complexity internally while providing simplified, personalized service to consumers.
2Productivity
If manual feature demonstration methods are used, then the system complexity is low, but the efficiency and effectiveness of feature presentation are reduced
Solution Approach 1:
The system replaces manual mechanical demonstration methods with automated electronic and computational systems. Instead of sales personnel physically demonstrating features, the system uses automated vehicle control, digital displays, sensors, and data processing algorithms to present features efficiently. This substitution dramatically increases presentation efficiency while managing automation complexity through integrated software-hardware systems.
Solution Approach 2:
The system performs preliminary analysis of consumer preferences and contexts before the actual feature presentation begins. By pre-processing consumer data, identifying relevant features in advance, and preparing customized presentation sequences, the system maximizes presentation efficiency during the actual interaction while the complexity of automation is distributed across pre-computation and real-time execution phases.
3Reliability
If generic feature presentations are provided to all consumers, then the system operation is simple, but the consumer engagement and sales conversion are suboptimal
Solution Approach 1:
The system provides customized feature presentations tailored to each individual consumer's preferences, needs, and contextual factors rather than uniform generic presentations. It analyzes local characteristics of each consumer (demographics, behavior patterns, expressed interests) and adjusts the feature demonstration accordingly, ensuring high relevance and engagement for each individual while managing complexity through automated personalization algorithms.
Solution Approach 2:
The system dynamically changes multiple parameters of the feature presentation based on consumer characteristics, including which features are demonstrated, the order of presentation, the duration of each demonstration, and the mode of presentation (visual, audio, interactive). These parameter adjustments are automatically calculated based on consumer data, improving sales effectiveness while managing complexity through systematic parameter optimization rather than manual customization.
Data Source
AI summary
Techniques for facilitating the autonomous presentation of a self-driving vehicle are provided. In one example, a method can include a system operatively coupled to a processor, where the system: determines a feature of a self-driving vehicle based on information regarding an entity in a pending transaction; determines a task to be performed by the self-driving vehicle based on the feature; and generates an instruction for the self-driving vehicle to perform the task.