Surrounding Vehicle Recognition via Inter-Vehicle Communication
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Solution Overview
Problem
Conventional vehicle recognition systems struggle to accurately determine the presence and position of surrounding vehicles, especially when not all vehicles are equipped with inter-vehicle communication devices, limiting advanced traveling control capabilities.
Innovation Solution
A surrounding vehicle recognition device that uses an inter-vehicle communication device to acquire and process vehicle state notification information, including position and speed data, to determine the recognition state of a leading vehicle, enabling the classification into standby, detecting, or tracing states, and incorporates this information into vehicle state notifications sent to surroundings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inter-vehicle communication is used to acquire vehicle state information, then the ability to recognize surrounding vehicles is improved, but the system cannot detect vehicles without communication devices
Solution Approach 1:
The system segments the vehicle recognition task into two parts: (1) direct communication-based recognition for equipped vehicles, and (2) indirect inference-based recognition for unequipped vehicles. By dividing the problem this way, the system can maintain high accuracy for communicating vehicles while still detecting non-communicating vehicles through spatial reasoning and gap analysis in the vehicle sequence.
Solution Approach 2:
The system uses an intermediary inference mechanism that acts as a bridge between direct communication data and the need to detect all vehicles. By analyzing communication patterns, position gaps, and travel behavior, the system infers the presence of vehicles without communication devices, thus mediating between limited direct information and comprehensive vehicle awareness.
2Adaptability or versatility
If the system determines the number of vehicles between self vehicle and a distant vehicle, then advanced traveling control is enabled, but the complexity of processing vehicle state information increases
Solution Approach 1:
The system performs preliminary sorting and organization of received vehicle state information before detailed analysis. By pre-processing the communication data to establish ordered vehicle sequences and identify gaps, the system reduces the computational complexity of subsequently determining the number of vehicles between positions, enabling advanced control without excessive processing burden.
3Extent of automation
If the system specifies a leading vehicle based on communication data, then rank traveling control is enabled, but vehicles without communication devices cannot be identified
Solution Approach 1:
The system uses feedback from multiple sources to verify leading vehicle identification: (1) direct position data from communicating vehicles, (2) gap analysis in the vehicle sequence, and (3) consistency checking with radar or other detection systems. This multi-feedback approach ensures reliable leading vehicle identification even when some vehicles lack communication devices, maintaining automated control reliability.
Data Source
AI summary
A behavior acquisition unit acquires behavior related information about a leading vehicle, which travels at a position closest to the self vehicle on an advancing route of the self vehicle. A front vehicle recognition determination unit determines, as a front vehicle recognition state, whether a leading vehicle is specified and whether a self vehicle travels immediately after the leading vehicle, according to the acquired behavior related information. A sending control unit is configured to cause transmission of the front vehicle recognition state and specifying information, which specifies the self vehicle and the leading vehicle, to surroundings of the self vehicle.