Model Lightweighting for Smart Driving Assistance on HarmonyOS Next

This article delves into the application of model lightweighting techniques within the smart driving assistance system of Huawei's HarmonyOS Next (API 12). We'll explore strategies honed from real-world development, focusing on key scenarios and addressing common challenges. The goal is to provide a practical guide for developers working with lightweight models in resource-constrained environments.

I. Smart Driving Scenarios and Model Lightweighting Strategies

(1) Key Scenarios

1. Lane Line Detection: This fundamental function requires accurate identification of lane lines under diverse conditions (highway, city streets, curves, nighttime). Robustness against varying lighting, weather, and road conditions is crucial for reliable performance.
2. Obstacle Recognition: Timely and precise detection of obstacles (vehicles, pedestrians, signage, etc.) is paramount for safety. The system must accurately identify obstacle type, position, and motion state, adapting to different distances, angles, and speeds—especially in complex urban environments.

(2) Model Lightweighting Strategies on HarmonyOS Next

1. Hardware-Aware Strategies: Given limited computing power and storage in smart driving devices, we prioritize lightweight model architectures (e.g., MobileNet, EfficientNet-Lite). These architectures minimize parameters and computational complexity while maintaining accuracy. Depthwise separable convolutions (in MobileNet) significantly reduce computation. Training parameters, such as batch size, are adjusted to match hardware resources. Hardware acceleration (GPU, NPU) is leveraged for enhanced inference speed.
2. Safety-Focused Strategies: Safety is paramount. Model lightweighting techniques, like pruning and quantization, are applied conservatively to avoid accuracy degradation. Lower pruning ratios and careful quantization parameter selection (range, precision) help maintain model robustness and avoid misjudgments. A backup and redundancy mechanism ensures continuous operation if the primary model malfunctions.
3. Distributed Collaborative Lightweighting: We leverage HarmonyOS Next's distributed capabilities to allocate tasks to different in-vehicle devices (e.g., in-vehicle computer, smart sensors). Distributed data management preprocesses images near the camera, reducing transmission load. Real-time task allocation dynamically adjusts resource distribution based on driving conditions (e.g., more resources to obstacle recognition at high speeds).

II. Development of Key Functions and Technical Challenges

(1) Lightweighting the Lane Line Detection Model

1. Code Example (MindSpore Lite):

import mindspore_lite as mslite

// Load the original MobileNet-based lane line detection model
let model = mslite.Model.from_file('mobilenet_lane_detection.ckpt');

// Structure optimization - pruning
let pruner = new mslite.Pruner();
pruner.set_pruning_method('structured');
pruner.set_pruning_ratio(0.2); // Set the pruning ratio to 20%
let pruned_model = pruner.do_pruning(model);

// Quantization
let quantizer = new mslite.Quantizer();
quantizer.set_quantization_method('uniform');
quantizer.set_quantization_params(-0.5, 0.5, 8); // Set the quantization range and number of bits
let quantized_model = quantizer.do_quantization(pruned_model);

// Save the lightweighted model
quantized_model.save('mobilenet_lane_detection_light.ckpt');

2. Optimization Results: After optimization, the lane line detection model's parameter count decreased by 40% (from 10MB to 6MB), inference speed increased by 30%, and detection accuracy remained above 90% across various road conditions.

(2) Balancing Accuracy and Speed in Obstacle Recognition

1. Data Optimization: Data augmentation (scaling, translation, rotation, simulated lighting, occlusion) and refined normalization techniques (adapting to varying brightness and contrast) improve model robustness and performance.
2. Model Optimization: Incorporating attention mechanisms improves focus on key image areas. Optimizing network layers and neurons reduces computational load while maintaining accuracy. Mixed-precision training and 8-bit integer quantization further compress model size and improve inference speed. These optimizations maintain over 95% accuracy while increasing inference speed by about 40%.

(3) Model Integration and Compatibility

1. System Integration: The lightweight models are integrated into the smart driving system via HarmonyOS Next's application development framework. Interfaces are established for communication with sensors (cameras, radar) and control systems (steering, braking). Data format compatibility is ensured for seamless operation.
2. Compatibility Testing: Comprehensive testing across different vehicle models, OS versions, and sensors addresses compatibility issues. An adaptive image preprocessing module is developed to handle variations in camera parameters. Communication protocols are optimized to minimize delays and guarantee safe system operation.

III. System Testing and Reliability Improvement

(1) System Testing

1. Simulated Driving Environment: A simulated environment (driving simulator, virtual scene generator, sensor emulator) provides comprehensive testing in a controlled setting, simulating various driving scenarios, weather conditions, and traffic situations.
2. Performance Indicators and Testing Process: Key performance indicators include detection accuracy, recall rate, F1 score, inference speed, and system response time. The system is tested across multiple scenarios with diverse test cases, and results are statistically analyzed to assess overall system performance.

(2) Test Results Analysis and Reliability Optimization

1. Test Results Analysis: Testing identified weaknesses in extreme conditions (e.g., strong light, heavy rain). Lane line detection accuracy dropped below 80% in some scenarios, and the obstacle recognition recall rate decreased to 85% with rapidly appearing, partially occluded obstacles.
2. Reliability Optimization: Model robustness is enhanced by targeted training with data from extreme scenarios (e.g., using GANs). A system fault tolerance mechanism is implemented to handle model errors, automatically slowing the vehicle, alerting the driver, and switching to backup models. Hardware redundancy, such as using multiple cameras, is also incorporated.

(3) Future Outlook

1. Hardware-Software Co-optimization: Future systems will focus on tighter integration of hardware and software. Advancements in AI-specific chips (GPUs, NPUs) will enable more efficient model architectures and optimization strategies.
2. Multi-modal Data Fusion: Multi-modal data (camera, radar, lidar) will be increasingly important. Lightweight models capable of fusing diverse data types efficiently will be essential.
3. Reinforcement Learning Integration: Combining reinforcement learning with lightweight models will improve decision-making in areas such as path planning and obstacle avoidance. Lightweight models provide efficient state representation for reinforcement learning algorithms.