Lane Occupancy Detection for Following Vehicles
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Solution Overview
Problem
Current driver assistance systems struggle to accurately determine the lane of following vehicles, especially in situations with high speed differences and curved road progressions, leading to potential fatal consequences during lane changes, as they fail to recognize the lane occupancy of vehicles behind the own vehicle.
Innovation Solution
A control system that uses environmental sensors to detect the course of the own lane and the lanes occupied by following vehicles, providing indications for safe or unsafe lane changes and enabling autonomous lane changes by processing data on vehicle positions and speed differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional environmental sensors detect following vehicles only by position and speed, then the detection process is simple, but the lane determination accuracy deteriorates
Solution Approach 1:
The system pre-detects and stores the course of the own lane over a predefined road section before making lane change decisions. This preliminary action of mapping the lane trajectory enables accurate lane determination of following vehicles by comparing their positions against the stored lane course, rather than relying on simple position detection alone.
Solution Approach 2:
The system transitions from detecting only position and speed (2D information) to incorporating lane course data (adding a dimensional aspect of trajectory). By storing and analyzing the spatial path of the own lane, the system can accurately determine which lane a following vehicle occupies, even on curved roads, thereby improving measurement precision without excessive complexity increase.
2Reliability
If the system monitors lane occupancy accurately, then lane change safety improves, but the processing time and computational load increase
Solution Approach 1:
The system continuously pre-detects and stores the course of the own lane over a predefined road section in advance. This preliminary mapping of lane trajectories allows for rapid lane occupancy determination during critical lane change moments, as the reference data is already available rather than needing to be computed in real-time during the decision process.
Solution Approach 2:
The system performs continuous background monitoring and storage of lane course data, creating a cushion of pre-processed information that can be quickly accessed during lane change operations. This beforehand preparation reduces the computational burden and processing time during critical safety decisions.
3Loss of information
If the system provides detailed lane occupancy information, then driver awareness improves, but the information overload risk increases
Solution Approach 1:
The system extracts and highlights only the most critical information for lane change safety - specifically, whether the target lane is occupied by a following vehicle. Rather than presenting all available sensor data and lane course information, the system isolates the essential occupancy status and safety indication, reducing information overload while maintaining completeness of safety-critical data.
Solution Approach 2:
Instead of presenting raw sensor data and requiring the driver to interpret complex information, the system inverts the approach by directly providing interpreted safety assessments and occupancy status. The system processes the complex data internally and presents simplified, action-oriented information to the driver, improving ease of operation while maintaining information completeness through accurate lane determination.
Data Source
AI summary
A Control system, which is adapted for application in a vehicle and intended to detect following vehicles on the basis of environmental data which are obtained from one or several environmental sensors disposed on the vehicle and which reflect the area in front of, laterally next to, and/or behind the vehicle. The control system is adapted to detect a course of the own lane of the vehicle and to store it over a predefined road section, and to detect one or several other vehicles participating in traffic behind the own vehicle with the environmental sensors. A lane associated with each other vehicle, in which the other vehicle(s) drive(s), is detected, taking the stored course of the own lane into account. An occupancy of the own lane and/or of at least one adjacent lane by the other vehicle(s) is determined.