Vehicle Longitudinal Control Using Environment-Aware Driver Handover
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Solution Overview
Problem
Current vehicle control systems do not effectively reduce the driver's workload by autonomously managing longitudinal vehicle movement in response to environmental factors, such as traffic jams or dynamic objects, without requiring explicit driver intervention.
Innovation Solution
A method that involves receiving environment-related information and driver input to generate a target parameter for controlling the vehicle's longitudinal movement, which is then sent to vehicle control units, allowing the vehicle to adjust speed and distance based on detected objects, using systems like radar sensors, LiDAR, and propulsion systems, with the driver initiating control handover by releasing the accelerator pedal or tapping the brake pedal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the vehicle autonomously manages longitudinal movement without driver intervention, then driver workload is reduced, but the system requires complex environment recognition and control mechanisms
Solution Approach 1:
The control system is segmented into distinct functional modules: environment recognition unit that detects objects and conditions, target parameter generation unit that calculates appropriate control parameters, and execution unit that implements longitudinal movement control. This modular segmentation reduces overall system complexity by allowing each module to be developed, tested, and maintained independently while working together to achieve autonomous vehicle control.
Solution Approach 2:
The system performs preliminary environment recognition and target parameter generation before actual vehicle control execution. The environment recognition unit continuously monitors and identifies objects ahead of time, and the target parameter generation unit pre-calculates appropriate control parameters based on recognized environmental conditions, enabling smooth and timely autonomous response without requiring complex real-time decision-making during critical control moments.
2Measurement precision
If the vehicle uses multiple sensors and detection systems to recognize objects, then measurement precision improves, but device complexity increases
Solution Approach 1:
Multiple sensor types (cameras, radar, LIDAR, ultrasonic sensors) are merged into a unified environment recognition system that detects and identifies objects in the vehicle's path. These diverse sensors are integrated and coordinated to work together, with their data fused to achieve comprehensive and accurate object detection. This merging approach improves measurement precision by leveraging the complementary strengths of different sensor technologies while managing complexity through integrated system architecture.
Solution Approach 2:
The environment recognition system is designed with multi-functionality to handle various detection tasks using the same sensor array. The system can identify different types of objects (pedestrians, vehicles, obstacles), determine their positions, velocities, and trajectories, and adapt to different environmental conditions all through a single unified detection system, reducing the need for separate specialized systems for each function.
Data Source
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AI summary
A Method for controlling a vehicle is disclosed, the method comprises the following steps: - step (S1): receiving at least one environment-related information from an environment-related source (3); - step (S2): receiving a driver input from a driver input interface (2); - step (S3): generating a target parameter for controlling the longitudinal movement according to the environment-related information; and - step (S4): providing the target parameter if the driver input fulfils a predetermined input condition. Further, a device, a vehicle, a computer program product and a storage device for carrying out the method are disclosed.