Road Surface Sensors for Dynamic Object Localization in HAV Maps
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Solution Overview
Problem
Current systems for highly automated vehicles (HAVs) face challenges in accurately detecting and predicting dynamic objects in their environment, particularly in complex scenarios like traffic nodes and intersections, due to limitations in sensor accuracy and visual range.
Innovation Solution
The method involves placing sensors with communications interfaces in the road surface to transmit overlap signals, allowing for the creation of a local environmental model that includes dynamic object positions and trajectories, which are then integrated into a digital map for improved localization and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle-internal sensors are used to detect dynamic objects, then the system can maintain independence and not require external infrastructure, but the detection accuracy and visual range are limited
Solution Approach 1:
The patent introduces road surface sensors as an intermediary element between the dynamic objects and the HAV's detection system. These sensors embedded in the road infrastructure act as mediators that detect dynamic objects and transmit this information to the HAV, thereby enhancing detection accuracy without requiring the vehicle itself to have extremely complex sensor systems. The road surface sensors serve as a distributed sensor network that complements vehicle-internal sensors.
2Measurement precision
If more sensors are placed in the road surface to improve detection accuracy, then the measurement precision increases, but the device complexity and infrastructure requirements increase
Solution Approach 1:
The patent divides the detection task into segments performed by multiple distributed road surface sensors rather than requiring a single complex sensor system. Each sensor independently detects dynamic objects in its local area and transmits information to a server, which then integrates the data. This segmentation allows the system to achieve high measurement precision through distributed sensing while managing infrastructure complexity through modular deployment.
Solution Approach 2:
The road surface sensors serve multiple functions: they detect dynamic objects, determine their positions, and provide this information to both the server and HAVs. This multi-functionality reduces the need for separate systems for different detection tasks, thereby managing infrastructure complexity while maintaining high measurement precision through a unified sensor network.
3Reliability
If a digital map with dynamic object information is created using road surface sensors, then the visual range and safety are improved, but the loss of time for data processing and transmission increases
Solution Approach 1:
The patent implements preliminary action by having road surface sensors continuously detect and track dynamic objects, maintaining an up-to-date digital map of the environment before the HAV needs this information. The server pre-processes sensor data and maintains the digital map in real-time, so that when the HAV requests environmental information, it is already available, reducing the time loss for data processing and enhancing driving safety.
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
This approach enhances the accuracy and robustness of dynamic object localization, enabling better prediction and safer autonomous driving by augmenting vehicle-internal sensor data with geo-referenced information from the road surface sensors.
Implementation Method 1
the sensors are surface-integrated magnetic-field sensors
Data Source
AI summary
An evaluation unit obtains from sensors in a roadway surface respective overlap signals (a) transmitted by the sensors via a communications interfaces of the sensors and (b) that include respective identifications of the sensors and information as to whether the respective sensors are currently overlapped by any dynamic objects above the sensors on the roadway surface, where the sensors are associated with respective geographical positions; ascertains a respective position of each of the at least one dynamic object situated on the roadway on the basis of the overlap signals in the form of a local environmental model of the road section; and transmits the local environmental model to the HAV in the form of a digital map identifying the at least one dynamic object at one or more respective positions of the map corresponding to the ascertained at least one respective position.