Autonomous Vehicle Teleoperation for Event-Aware Trajectory Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles struggle to effectively detect and respond to changing environments and events, such as lane closures or obstacles, without adequate information dissemination to other vehicles, leading to inefficiencies and safety risks.
Innovation Solution
Implementing a system that includes a fleet of autonomous vehicles connected to a service platform, utilizing sensors, localizers, perception engines, and planners to detect events, calculate optimal trajectories, and enable teleoperation when necessary, with redundancy in sensor fields to ensure reliable navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles operate independently without information sharing, then each vehicle maintains operational independence, but fleet-wide efficiency and safety are reduced due to lack of event dissemination
Solution Approach 1:
The system implements feedback loops where sensor data and events detected by one autonomous vehicle are transmitted to the service platform, which then disseminates this information to other vehicles in the fleet. This creates a continuous information feedback cycle that improves fleet-wide awareness and efficiency without compromising individual vehicle independence.
Solution Approach 2:
The service platform acts as an intermediary between autonomous vehicles, receiving event data from sensors and distributing relevant information to other vehicles. This mediator approach enables information sharing across the fleet while maintaining operational independence of individual vehicles.
2Reliability
If teleoperation is used to handle complex events, then decision accuracy improves, but response time increases due to communication delays
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and identifying potential events before they require teleoperation intervention. The service platform pre-analyzes incoming data streams and prepares potential responses, so when teleoperation is needed, the decision-making process is already partially complete, reducing overall response time while maintaining accuracy.
3Reliability
If sensor redundancy is implemented to ensure reliable navigation, then detection reliability improves, but system complexity and cost increase
Solution Approach 1:
The system merges sensor data from multiple sources and combines detection results through data fusion algorithms. By integrating information from various sensors and combining their outputs, the system achieves enhanced detection reliability without proportionally increasing system complexity, as the processing is centralized and optimized.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
A system, an apparatus or a process may be configured to implement an application that applies artificial intelligence and/or machine-learning techniques to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (e.g., one or more of a planner of an autonomous vehicle, a simulator, or a teleoperator) to undertake based on suboptimal autonomous vehicle performance and/or changes in detected sensor data (e.g., new buildings, landmarks, potholes, etc.). The application may determine a subset of trajectories based on a number of decisions and interactions when resolving an anomaly due to an event or condition. The application may use aggregated sensor data from multiple autonomous vehicles to assist in identifying events or conditions that might affect travel (e.g., using semantic scene classification). An optimal subset of trajectories may be formed based on recommendations responsive to semantic changes (e.g., road construction).