Autonomous Vehicle Corridor Selection for Traffic Redirection
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating through traffic redirections, particularly when corridors are not clearly defined by lane lines, leading to ambiguities that existing systems struggle to resolve, potentially resulting in unsafe maneuvers.
Innovation Solution
The method involves using pre-stored map information, data from a perception system to identify traffic redirections, and processors to select the appropriate corridor based on traffic flow analysis, signage, and vehicle behavior, with the option to request remote instructions if ambiguity persists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles use pre-stored map information for navigation, then navigation efficiency is improved, but the system cannot detect traffic redirections not recorded in maps
Solution Approach 1:
The system performs preliminary detection of traffic redirection objects (cones, barrels, signs) using the perception system before navigation decisions are made. This allows the vehicle to identify unrecorded redirections in real-time and adjust its path accordingly, combining the efficiency of pre-stored maps with the adaptability of live detection.
2Device complexity
If the vehicle relies solely on map information, then system complexity is reduced, but safety is compromised due to inability to respond to construction and incidents
Solution Approach 1:
The system merges pre-stored map information with real-time perception data from sensors detecting cones, barrels, and signs. This combination allows the vehicle to maintain the simplicity and efficiency of map-based navigation while adding safety through real-time detection of construction zones and incidents that are not yet recorded in maps.
3Adaptability or versatility
If the vehicle uses perception system data to identify traffic redirections, then adaptability to unrecorded situations is improved, but processing complexity increases
Solution Approach 1:
The system extracts only the critical elements needed for navigation decisions from the full perception data stream - specifically identifying traffic redirection objects (cones, barrels, signs) and their spatial relationships. This extraction approach maintains high adaptability to unrecorded situations while reducing processing complexity by focusing only on relevant features rather than analyzing all environmental data.
4Measurement precision
If the vehicle must determine traffic flow direction through analysis, then accuracy of corridor selection is improved, but response time is reduced
Solution Approach 1:
The system uses the vehicle's own perception data and positional information to determine traffic flow direction, rather than relying on external infrastructure or complex multi-vehicle communication. By analyzing the positions of redirection objects relative to the vehicle's own trajectory and using map data about road geometry, the system achieves accurate corridor selection independently and quickly, minimizing response time loss.
Data Source
AI summary
The technology relates to controlling a vehicle in an autonomous driving mode, the method. For instance, a vehicle may be maneuvered in the autonomous driving mode using pre-stored map information identifying traffic flow directions. Data may be received from a perception system of the vehicle identifying objects in an external environment of the vehicle related to a traffic redirection not identified the map information. The received data may be used to identify one or more corridors of a traffic redirection. One of the one or more corridors may be selected based on a direction of traffic flow through the selected corridor. The vehicle may then be controlled in the autonomous driving mode to enter and follow the selected one of the one or more corridors based on the determined direction of flow of traffic through each of the one or more corridors.