Breakthroughs in Large Models and Multimodal Perception
- Enhanced Environmental Understanding: DeepSeek’s accumulated technologies in natural language processing (NLP) and multimodal learning can enhance the autonomous driving system’s ability to understand complex scenarios semantically. For example, by integrating LiDAR, cameras, and millimeter-wave radar data, and combining language models to infer traffic signs and pedestrian intentions, more precise decision-making can be achieved.
- Solving the Long Tail Problem: With Few-shot Learning (small sample learning) capabilities based on large models, rare scenarios (e.g., extreme weather, special obstacles) can be effectively handled, reducing the limitations of traditional systems that rely on vast amounts of annotated data.
Efficient Algorithms and Computing Power Optimization
- Real-Time Performance Improvement: DeepSeek’s model compression technologies (such as knowledge distillation and quantization) and lightweight network design can reduce the computing delay of the autonomous driving system, meeting the computational constraints of in-vehicle chips (such as Nvidia Orin, Horizon Journey), and enabling lower power consumption edge computing.
- Dynamic Path Planning: The combination of Reinforcement Learning (RL) and Imitation Learning can optimize the vehicle’s path selection in complex traffic flows, for example, using game theory models to handle unprotected left turns and merging scenarios.
Revolution in Simulation and Virtual Testing
- High-Fidelity Simulation Environments: DeepSeek’s generative AI technology can create highly realistic virtual test scenarios (e.g., extreme weather, accident simulations), accelerating algorithm iteration. Industry data suggests that simulation testing can cover 99% of extreme cases, reducing road testing costs by 90%.
- Data Synthesis and Augmentation: Generative Adversarial Networks (GANs) can generate scarce scenario data (e.g., pedestrians suddenly crossing), supplementing the lack of real-world data and enhancing model robustness.
Vehicle-Road Collaboration and V2X Communication
- Global Decision Optimization: DeepSeek’s distributed AI architecture could drive the integration of vehicle-road-cloud systems, for instance, by using roadside units (RSU) to upload real-time road information and combining cloud-based large models for global scheduling, alleviating local traffic congestion.
- Communication Efficiency Improvement: AI-driven signal compression technologies (such as deep encoding and decoding) can reduce V2X communication latency, meeting the autonomous driving requirement for millisecond-level response times.
Balancing Safety and Ethics
- Enhanced Explainability: Through Attention mechanisms, the model’s decision-making logic can be visualized, helping engineers understand the AI’s “black box” behavior and meeting traceability requirements in ISO 26262 functional safety standards.
- Ethical Decision Framework: Combining causal inference techniques to create ethically compliant emergency obstacle avoidance strategies (e.g., quantifying trade-offs in the trolley problem) provides technical support for policy-making.
Cost and Commercialization Acceleration
- Reduced Hardware Dependency: By optimizing algorithms, reliance on high-line LiDARs can be reduced (e.g., Waymo’s early systems cost over $80,000, while Tesla’s pure vision solution costs only a few thousand dollars), pushing forward the commercialization of L4 autonomous driving.
- Continuous OTA Evolution: With lifelong learning capabilities based on large models, vehicles can continuously optimize driving strategies via OTA (over-the-air) updates, extending the technology’s lifecycle.
