Autonomous Vehicle Passenger Identification for Confident Pickups
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying the passenger to be picked up, which can lead to miscommunication and improper navigation, especially in complex environments, affecting riders with disabilities or other passengers.
Innovation Solution
A holistic approach using signal synthesis from different sensors, agent prediction, and situational awareness to identify the passenger with confidence, providing adaptive navigation through visual, audible, and haptic cues, and employing rider support tools via mobile devices for wayfinding assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor types are used to identify the customer, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple sensor types (cameras, LIDAR, radar, ultrasonic sensors) into an integrated perception system that processes data from all sensors simultaneously. This merging approach allows the system to achieve high identification accuracy by cross-validating signals from different sensors while managing complexity through unified data processing architecture.
Solution Approach 2:
The perception system is designed to perform multiple functions using the same sensor array: detecting customer presence, identifying the specific customer, determining customer location, and monitoring environmental conditions. This multi-functionality reduces the need for separate specialized sensors for each task, thereby improving identification accuracy without proportionally increasing system complexity.
2Reliability
If sensor information is processed to determine customer likelihood, then identification reliability is improved, but computational requirements increase
Solution Approach 1:
The system pre-processes sensor data by creating candidate lists of potential customers based on basic detection criteria before performing detailed likelihood analysis. This preliminary filtering reduces the volume of data requiring intensive computational processing, thereby maintaining high identification reliability while reducing energy consumption during the matching phase.
Solution Approach 2:
The perception system automatically prioritizes processing of sensor data based on confidence levels and temporal urgency, with high-confidence detections requiring minimal additional processing. The system self-regulates computational resource allocation, directing more energy to uncertain cases while efficiently handling clear detections, thus optimizing the balance between reliability and energy use.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances passenger identification and navigation accuracy, ensuring efficient and accessible pickup and drop-off experiences for diverse riders, including those with disabilities, by providing real-time location awareness and tailored wayfinding support.
Implementation Method 1
The one or more sensors of the perception system may include one or more of lidar, camera, radar or acoustical sensors
Implementation Method 2
The one or more sensors of the perception system may include one or more of lidar, camera, radar or acoustical sensors
Data Source
AI summary
The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.