Learned-Route Vehicle Control for Semi-Autonomous Driving
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Solution Overview
Problem
Existing vehicle control systems struggle to autonomously or semi-autonomously navigate familiar routes with varying conditions, such as intersections and unexpected obstacles, while maintaining low sensor requirements and training efforts, and often require human intervention for unforeseen events.
Innovation Solution
A vehicle control system that uses deep learning algorithms to learn road features and traffic conditions through repetitive drives, allowing semi-autonomous control on familiar routes, with the option for human takeover and incorporating sensors like cameras, LIDAR, and RADAR for environment detection, and vehicle-to-vehicle communication for enhanced safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the system uses deep learning algorithms to learn road features through repetitive drives, then the autonomy level and route familiarity improve, but the training time and initial learning passes increase
Solution Approach 1:
The system performs preliminary learning actions by capturing and storing road feature data during initial driving passes before autonomous operation is needed. The control system learns road curvature, lane markings, and contour features in advance during multiple training drives, so that when autonomous mode is activated, the vehicle already has pre-processed route knowledge ready for immediate use.
2Measurement precision
If the system captures and processes multiple types of sensor data (image data, sensor data) to learn road features, then the measurement precision and route learning accuracy improve, but the device complexity and processing requirements increase
Solution Approach 1:
The system segments the complex sensing and processing tasks into distinct functional modules: image data capture by cameras, sensor data capture by dedicated sensors, feature extraction for road curvature and lane markings, and control execution. This segmentation allows each component to specialize in specific data types and processing tasks, improving overall measurement precision while managing device complexity through modular architecture.
Solution Approach 2:
The control system is designed with multi-functional capability to process both image data from cameras and sensor data from other sensors through a unified learning algorithm framework. This universal processing approach allows the same control system to handle multiple data types and sensor configurations, reducing overall system complexity while maintaining high route learning accuracy.
3Reliability
If the system allows human driver to take over control at any time, then the safety and user control improve, but the responsiveness and automated operation efficiency decrease
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor driving conditions and system performance. When the driver takes over control, the system provides feedback about the learned route and detected features, and when returning to autonomous mode, the system validates that conditions remain appropriate for automated operation. This feedback loop ensures safety through human oversight while maintaining responsive automated operation through rapid state transitions.
Data Source
AI summary
A vehicular control system for controlling a vehicle includes a vehicle control, an acceleration sensor and a camera. The vehicle control includes an image processor for processing image data captured by the camera as the vehicle is driven along a route by a driver of the vehicle. The vehicle control detects traffic and road topography and determines acceleration of the vehicle as the vehicle is driven along the route by the driver. The vehicle control learns the route during multiple repetitive drives of the route by the driver of the vehicle. The vehicle control increases a confidence level of the learned route during multiple repetitive drives of the route by the vehicle. When the confidence level exceeds a threshold value, the vehicle control is operable to at least semi-autonomously control the vehicle to drive the vehicle along the route.