VLM视觉语言模型DMS应用探索：论文解读与实现

论文信息

• 标题： Exploration of VLMs for Driver Monitoring Systems Applications
• 作者： Paola Natalia Cañas Rodriguez 等
• 会议： 16th ITS European Congress, Seville, Spain, 19-21 May 2025
• 链接： https://arxiv.org/abs/2503.12281
• 领域： Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

核心创新

首次将视觉语言模型（VLM）应用于驾驶员监控系统（DMS），探索了VLM在驾驶员行为识别、疲劳检测、分心检测等任务中的潜力。

传统DMS vs VLM方法对比

维度	传统DMS	VLM-DMS
开发模式	数据收集→标注→训练	Prompt Engineering
泛化能力	受限于训练数据	Zero-shot能力
新任务适应	重新训练	修改Prompt
开发周期	数月	数天
可解释性	黑盒	自然语言解释

技术方案

1. VLM架构选择

"""
VLM-DMS系统架构

支持多种VLM骨干：
- CLIP (OpenAI)
- BLIP-2 (Salesforce)
- LLaVA (LLaMA + ViT)
- GPT-4V / Gemini Vision
"""

import torch
import torch.nn as nn
from transformers import (
    CLIPModel, CLIPProcessor,
    Blip2ForConditionalGeneration, Blip2Processor,
    LlavaForConditionalGeneration, LlavaProcessor
)
from typing import List, Dict, Tuple, Optional
from enum import Enum
import numpy as np


class VLMBackbone(Enum):
    """VLM骨干网络枚举"""
    CLIP = "clip"
    BLIP2 = "blip2"
    LLAVA = "llava"


class VLMBasedDMS:
    """
    基于VLM的驾驶员监控系统
    
    支持任务：
    - 驾驶员行为识别
    - 疲劳检测
    - 分心检测
    - 危险行为识别
    """
    
    def __init__(
        self,
        backbone: VLMBackbone = VLMBackbone.BLIP2,
        device: str = "cuda",
        use_quantization: bool = True
    ):
        self.backbone = backbone
        self.device = device
        
        # 加载模型
        self._load_model(backbone, use_quantization)
        
        # DMS行为标签
        self.behavior_labels = [
            "safe driving",
            "distracted by phone",
            "distracted by passenger",
            "adjusting radio",
            "drinking",
            "eating",
            "reaching behind",
            "hair/makeup",
            "talking to passenger",
            "yawning",
            "eyes closed",
            "looking away"
        ]
        
        # 预定义Prompt模板
        self.prompt_templates = {
            'behavior': "What is the driver doing? Choose from: {labels}. Answer with the most appropriate behavior.",
            'fatigue': "Is this driver showing signs of fatigue or drowsiness? Answer yes or no and explain why.",
            'distraction': "Is the driver distracted? If yes, what is causing the distraction?",
            'safety': "Are there any safety concerns with the driver's current behavior? List them."
        }
    
    def _load_model(self, backbone: VLMBackbone, use_quantization: bool):
        """加载VLM模型"""
        if backbone == VLMBackbone.CLIP:
            self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
            self.model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
            
        elif backbone == VLMBackbone.BLIP2:
            self.processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
            self.model = Blip2ForConditionalGeneration.from_pretrained(
                "Salesforce/blip2-opt-2.7b",
                torch_dtype=torch.float16 if use_quantization else torch.float32
            )
            
        elif backbone == VLMBackbone.LLAVA:
            self.processor = LlavaProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
            self.model = LlavaForConditionalGeneration.from_pretrained(
                "llava-hf/llava-1.5-7b-hf",
                torch_dtype=torch.float16 if use_quantization else torch.float32
            )
        
        self.model.to(self.device)
        self.model.eval()
    
    def encode_image(self, image) -> torch.Tensor:
        """编码图像"""
        if isinstance(image, np.ndarray):
            from PIL import Image
            image = Image.fromarray(image)
        
        inputs = self.processor(images=image, return_tensors="pt")
        return inputs.to(self.device)
    
    def classify_behavior(
        self,
        image,
        return_confidence: bool = True
    ) -> Dict:
        """
        分类驾驶员行为
        
        Args:
            image: 输入图像 (PIL Image 或 numpy array)
            return_confidence: 是否返回置信度
        
        Returns:
            result: {
                'behavior': 行为标签,
                'confidence': 置信度,
                'explanation': 解释
            }
        """
        # 构建Prompt
        labels_str = ", ".join(self.behavior_labels)
        prompt = self.prompt_templates['behavior'].format(labels=labels_str)
        
        # 编码
        inputs = self.processor(
            images=image,
            text=prompt,
            return_tensors="pt"
        ).to(self.device)
        
        # 推理
        with torch.no_grad():
            if self.backbone == VLMBackbone.CLIP:
                # CLIP分类
                outputs = self.model(**inputs)
                logits_per_image = outputs.logits_per_image
                probs = logits_per_image.softmax(dim=1)
                
                # 构建文本输入
                text_inputs = self.processor(
                    text=self.behavior_labels,
                    padding=True,
                    return_tensors="pt"
                ).to(self.device)
                
                # 计算相似度
                image_features = self.model.get_image_features(inputs['pixel_values'])
                text_features = self.model.get_text_features(**text_inputs)
                
                similarity = (image_features @ text_features.T).softmax(dim=1)
                top_idx = similarity.argmax().item()
                
                return {
                    'behavior': self.behavior_labels[top_idx],
                    'confidence': similarity[0, top_idx].item(),
                    'all_probs': {
                        label: similarity[0, i].item() 
                        for i, label in enumerate(self.behavior_labels)
                    }
                }
            
            else:
                # 生成式VLM
                outputs = self.model.generate(
                    **inputs,
                    max_new_tokens=100,
                    do_sample=False
                )
                
                response = self.processor.decode(outputs[0], skip_special_tokens=True)
                
                # 解析响应
                detected_behavior = self._parse_behavior(response)
                
                return {
                    'behavior': detected_behavior,
                    'response': response,
                    'raw_output': response
                }
    
    def detect_fatigue(self, image) -> Dict:
        """
        检测疲劳
        
        Args:
            image: 输入图像
        
        Returns:
            result: {
                'is_fatigued': bool,
                'indicators': List[str],
                'confidence': float
            }
        """
        prompt = self.prompt_templates['fatigue']
        
        inputs = self.processor(
            images=image,
            text=prompt,
            return_tensors="pt"
        ).to(self.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=150,
                do_sample=False
            )
        
        response = self.processor.decode(outputs[0], skip_special_tokens=True)
        
        # 解析响应
        is_fatigued = 'yes' in response.lower()[:20]
        
        # 提取疲劳指标
        indicators = self._extract_fatigue_indicators(response)
        
        return {
            'is_fatigued': is_fatigued,
            'indicators': indicators,
            'response': response
        }
    
    def detect_distraction(self, image) -> Dict:
        """
        检测分心
        
        Args:
            image: 输入图像
        
        Returns:
            result: {
                'is_distracted': bool,
                'distraction_type': str,
                'confidence': float
            }
        """
        prompt = self.prompt_templates['distraction']
        
        inputs = self.processor(
            images=image,
            text=prompt,
            return_tensors="pt"
        ).to(self.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=150,
                do_sample=False
            )
        
        response = self.processor.decode(outputs[0], skip_special_tokens=True)
        
        return {
            'is_distracted': 'yes' in response.lower()[:20],
            'distraction_type': self._extract_distraction_type(response),
            'response': response
        }
    
    def _parse_behavior(self, response: str) -> str:
        """解析行为"""
        response_lower = response.lower()
        
        for label in self.behavior_labels:
            if label in response_lower:
                return label
        
        return "unknown"
    
    def _extract_fatigue_indicators(self, response: str) -> List[str]:
        """提取疲劳指标"""
        indicators = []
        
        fatigue_keywords = {
            'yawning': 'yawning',
            'eyes closed': 'eyes_closed',
            'blinking': 'excessive_blinking',
            'head nodding': 'head_nodding',
            'drowsy': 'drowsy_expression'
        }
        
        response_lower = response.lower()
        for keyword, indicator in fatigue_keywords.items():
            if keyword in response_lower:
                indicators.append(indicator)
        
        return indicators
    
    def _extract_distraction_type(self, response: str) -> str:
        """提取分心类型"""
        distraction_types = {
            'phone': 'phone_use',
            'passenger': 'passenger_distraction',
            'radio': 'radio_adjustment',
            'eating': 'eating',
            'drinking': 'drinking',
            'mirror': 'mirror_checking'
        }
        
        response_lower = response.lower()
        for keyword, dtype in distraction_types.items():
            if keyword in response_lower:
                return dtype
        
        return 'unknown'


# 多帧时序融合
class TemporalVLM_DMS:
    """时序VLM-DMS系统"""
    
    def __init__(
        self,
        vlm: VLMBasedDMS,
        window_size: int = 30,  # 1秒窗口（30fps）
        fusion_strategy: str = 'voting'
    ):
        self.vlm = vlm
        self.window_size = window_size
        self.fusion_strategy = fusion_strategy
        
        # 历史记录
        self.history = {
            'behaviors': [],
            'fatigue_scores': [],
            'distraction_scores': []
        }
    
    def process_frame(self, frame) -> Dict:
        """
        处理单帧
        
        Args:
            frame: 输入帧
        
        Returns:
            result: 融合后结果
        """
        # VLM推理
        behavior_result = self.vlm.classify_behavior(frame)
        fatigue_result = self.vlm.detect_fatigue(frame)
        distraction_result = self.vlm.detect_distraction(frame)
        
        # 更新历史
        self.history['behaviors'].append(behavior_result)
        self.history['fatigue_scores'].append(
            1.0 if fatigue_result['is_fatigued'] else 0.0
        )
        self.history['distraction_scores'].append(
            1.0 if distraction_result['is_distracted'] else 0.0
        )
        
        # 限制历史长度
        if len(self.history['behaviors']) > self.window_size:
            self.history['behaviors'].pop(0)
            self.history['fatigue_scores'].pop(0)
            self.history['distraction_scores'].pop(0)
        
        # 时序融合
        return self._temporal_fusion()
    
    def _temporal_fusion(self) -> Dict:
        """时序融合"""
        if len(self.history['behaviors']) < 5:
            return {
                'behavior': 'collecting',
                'fatigue_score': 0.0,
                'distraction_score': 0.0
            }
        
        # 行为投票
        behaviors = [r['behavior'] for r in self.history['behaviors']]
        from collections import Counter
        behavior_counts = Counter(behaviors)
        final_behavior = behavior_counts.most_common(1)[0][0]
        
        # 疲劳评分
        fatigue_score = np.mean(self.history['fatigue_scores'])
        
        # 分心评分
        distraction_score = np.mean(self.history['distraction_scores'])
        
        return {
            'behavior': final_behavior,
            'behavior_confidence': behavior_counts.most_common(1)[0][1] / len(behaviors),
            'fatigue_score': fatigue_score,
            'distraction_score': distraction_score,
            'alert_fatigue': fatigue_score > 0.5,
            'alert_distraction': distraction_score > 0.5
        }


# 测试
if __name__ == "__main__":
    # 创建VLM-DMS
    vlm_dms = VLMBasedDMS(
        backbone=VLMBackbone.BLIP2,
        device="cuda",
        use_quantization=True
    )
    
    # 创建时序系统
    temporal_dms = TemporalVLM_DMS(vlm_dms, window_size=30)
    
    print("VLM-DMS系统初始化完成")
    print(f"支持行为标签: {len(vlm_dms.behavior_labels)} 个")
    print(f"Prompt模板: {list(vlm_dms.prompt_templates.keys())}")

2. Prompt Engineering

class DMSPromptEngineer:
    """DMS Prompt工程"""
    
    def __init__(self):
        # 安全相关Prompt
        self.safety_prompts = {
            'critical': [
                "URGENT: Is the driver's eyes closed? This is a critical safety issue.",
                "EMERGENCY: Detect if the driver is unconscious or unresponsive.",
                "ALERT: Is the driver showing signs of falling asleep?"
            ],
            'warning': [
                "Is the driver looking away from the road for an extended period?",
                "Detect if the driver is using a mobile phone while driving.",
                "Is the driver reaching for something in the back seat?"
            ],
            'info': [
                "Describe the driver's current posture and attention state.",
                "What objects is the driver interacting with?",
                "Is the driver wearing a seatbelt correctly?"
            ]
        }
    
    def get_prompt_for_scenario(
        self,
        scenario: str,
        severity: str = 'warning'
    ) -> str:
        """根据场景获取Prompt"""
        import random
        prompts = self.safety_prompts.get(severity, [])
        return random.choice(prompts) if prompts else ""
    
    def build_multitask_prompt(self) -> str:
        """构建多任务Prompt"""
        return """
        Analyze this driver image and answer:
        1. Behavior: What is the driver doing? (safe driving, distracted, fatigued, other)
        2. Fatigue Level: Rate from 0-10 (0=alert, 10=extremely fatigued)
        3. Distraction: Is the driver distracted? If yes, by what?
        4. Safety Concerns: List any safety issues.
        5. Recommended Action: What should the system do? (no action, warning, critical alert)
        
        Format your answer as JSON.
        """

实验结果

Driver Monitoring Dataset (DMD)评估

任务	传统CNN	VLM (BLIP-2)	VLM (LLaVA)
行为识别	87.3%	82.5%	85.1%
疲劳检测	84.2%	79.8%	81.3%
分心检测	89.1%	85.6%	87.2%
Zero-shot新行为	12.5%	68.3%	72.1%

优势分析

1. 零样本能力： 无需训练即可识别新行为
2. 可解释性： 自然语言解释决策原因
3. 灵活性： 通过Prompt快速适应新任务
4. 多任务： 单个模型处理多种任务

挑战

1. 延迟： 大型VLM推理时间较长
2. 资源： 需要较多GPU内存
3. 稳定性： 输出格式可能不一致
4. 边缘部署： 难以直接部署到嵌入式设备

IMS应用启示

适用场景

场景	传统DMS	VLM-DMS	建议
量产车型	✅ 推荐	⚠️ 实验性	传统CNN为主
新行为扩展	❌ 需重训练	✅ 快速适应	VLM辅助
开发阶段	⚠️ 周期长	✅ 快速原型	VLM优先
离线分析	⚠️ 有限	✅ 深度分析	VLM优势

混合方案建议

class HybridDMS:
    """混合DMS系统"""
    
    def __init__(self):
        # 轻量级CNN用于实时
        self.realtime_cnn = LightweightCNN()
        
        # VLM用于离线分析和异常处理
        self.offline_vlm = VLMBasedDMS()
        
    def process(self, frame):
        # 实时CNN推理
        cnn_result = self.realtime_cnn(frame)
        
        # 低置信度时使用VLM
        if cnn_result['confidence'] < 0.7:
            vlm_result = self.offline_vlm.classify_behavior(frame)
            return self._merge_results(cnn_result, vlm_result)
        
        return cnn_result

总结

核心贡献

1. 首次探索VLM在DMS的应用
2. 验证了零样本行为识别能力
3. 提出了混合部署方案

未来方向

1. 边缘优化： 模型蒸馏、量化
2. 多模态融合： VLM + 生理信号
3. 主动学习： 从VLM反馈改进CNN
4. 实时性提升： 模型架构优化

---

参考资源：

• arXiv:2503.12281
• BLIP-2 Paper
• Driver Monitoring Dataset