根据deepseek模型微调训练自动驾驶模型及数据集的思路

以下是使用DeepSeek模型微调训练自动驾驶模型的详细步骤和代码示例。本流程假设你已有自动驾驶领域的数据集（如驾驶指令、传感器数据等），并基于PyTorch框架实现。

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Step 1: 环境准备 # 安装依赖库 pip install torch transformers datasets numpy pandas

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Step 2: 数据准备

假设数据集格式为JSON，包含输入文本（传感器/场景描述）和输出控制指令：

// data/train.json [ { "input": "前方10米有行人，当前车速30km/h，车道居中", "output": "减速至20km/h，保持车道" }, // 更多样本... ]

构建数据集加载器：

from datasets import load_dataset from transformers import AutoTokenizer # 加载数据集 dataset = load_dataset('json', data_files={'train': 'data/train.json', 'val': 'data/val.json'}) # 初始化分词器 tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-base-1.3B") tokenizer.pad_token = tokenizer.eos_token # 设置填充token # 数据预处理函数 def preprocess_function(examples): inputs = [f"自动驾驶指令生成: {text}" for text in examples["input"]] model_inputs = tokenizer( inputs, max_length=512, truncation=True, padding="max_length" ) # 处理标签 labels = tokenizer( examples["output"], max_length=128, truncation=True, padding="max_length" ) model_inputs["labels"] = labels["input_ids"] return model_inputs # 应用预处理 tokenized_dataset = dataset.map(preprocess_function, batched=True)

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Step 3: 模型加载与适配 from transformers import AutoModelForCausalLM, TrainingArguments, Trainer # 加载预训练模型 model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-base-1.3B") # 修改模型头部（适配自动驾驶任务） if model.config.vocab_size != len(tokenizer): model.resize_token_embeddings(len(tokenizer))

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Step 4: 训练配置 training_args = TrainingArguments( output_dir="./autopilot_model", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=4, per_device_eval_batch_size=4, num_train_epochs=3, weight_decay=0.01, logging_steps=50, fp16=True, # 启用混合精度训练 save_strategy="epoch", report_to="tensorboard" ) # 初始化Trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_dataset["train"], eval_dataset=tokenized_dataset["val"], tokenizer=tokenizer, )

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Step 5: 模型微调 # 开始训练 trainer.train() # 保存最终模型 model.save_pretrained("./autopilot_final") tokenizer.save_pretrained("./autopilot_final")

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Step 6: 推理测试 from transformers import pipeline # 创建推理管道 autopilot_pipe = pipeline( "text-generation", model="./autopilot_final", tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1 ) # 测试样例 input_text = "自动驾驶指令生成: 前方100米红灯，当前车速50km/h" generated = autopilot_pipe( input_text, max_length=128, temperature=0.7, num_return_sequences=1 ) print(generated[0]['generated_text']) # 输出示例: "减速至停车线前，等待绿灯"

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Step 7: 部署优化（可选） 模型量化： from transformers import BitsAndBytesConfig quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16 ) quant_model = AutoModelForCausalLM.from_pretrained("./autopilot_final", quantization_config=quant_config) ONNX导出： from transformers.convert_graph_to_onnx import convert convert(framework="pt", model="./autopilot_final", output="autopilot.onnx", opset=12)

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关键优化技巧

数据增强：

添加噪声：模拟传感器误差场景扩展：生成雨天/雾天等特殊场景描述 def add_noise(text, noise_level=0.1): words = text.split() # 随机替换部分词汇 return " ".join([w if random.random() > noise_level else "[UNK]" for w in words])

多模态融合（如结合视觉特征）：

# 示例：融合图像特征 vision_encoder = AutoModel.from_pretrained("google/vit-base-patch16-224") image_features = vision_encoder(images).last_hidden_state.mean(dim=1) # 将视觉特征注入语言模型 combined_features = torch.cat([text_embeddings, image_features.unsqueeze(1)], dim=1)

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安全关键设计 冗余校验模块： def safety_check(predicted_command): # 实现速度限制、碰撞检测等安全逻辑 if "急加速" in predicted_command and current_speed > 60: return "建议维持当前车速" return predicted_command 实时性监控： import time start_time = time.time() generated = autopilot_pipe(...) if (time.time() - start_time) > 0.1: # 超过100ms触发警告 print("WARNING: 推理延迟过高！")

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效果评估指标 from rouge import Rouge rouge = Rouge() def evaluate(predictions, references): # ROUGE指标 scores = rouge.get_scores(predictions, references, avg=True) # 自定义安全评分 safety_score = sum([1 if "危险" not in p else 0 for p in predictions])/len(predictions) return { "rouge": scores, "safety": safety_score }

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通过以上流程，你可以基于DeepSeek模型构建一个针对自动驾驶场景的指令生成系统。实际应用中需注意：

数据质量：确保训练数据覆盖各类道路场景实时性测试：在目标硬件上验证推理速度安全机制：必须加入多层冗余安全检查持续学习：定期用新数据更新模型 建议在实际部署前进行严格仿真测试，可使用CARLA等自动驾驶仿真平台验证模型行为。

以下是一个自动生成自动驾驶训练数据集的Python方案，包含多种典型驾驶场景的模拟数据生成逻辑。该数据集将包含文本指令、传感器数据（模拟）和对应的控制指令。

import json import random from faker import Faker import numpy as np from tqdm import tqdm fake = Faker('zh_CN') def generate_scenario(scene_type): """生成基础场景描述""" base_scene = { "weather": random.choice(["晴天", "小雨", "雾天", "夜间"]), "road_type": random.choice(["城市道路", "高速公路", "乡村道路", "隧道"]), "speed": random.randint(20, 120) } # 根据不同场景类型添加特定要素 if scene_type == "normal": return { **base_scene, "event": "保持车道行驶", "obstacles": [] } elif scene_type == "obstacle": return { **base_scene, "event": random.choice(["行人横穿", "车辆加塞", "动物闯入"]), "distance": random.randint(5, 100), "obstacle_speed": random.randint(0, 10) if base_scene["road_type"] != "高速公路" else 0 } elif scene_type == "traffic_control": return { **base_scene, "event": random.choice(["红灯", "施工路段", "交警指挥"]), "distance": random.randint(10, 200) } elif scene_type == "emergency": return { **base_scene, "event": random.choice(["爆胎", "刹车失灵", "前方事故"]), "severity": random.choice(["轻度", "中度", "重度"]) } def generate_sensor_data(scenario): """生成模拟传感器数据""" sensor = { "camera": { "front": np.random.rand(256, 256, 3).tolist(), # 模拟图像数据 "left": np.random.rand(128, 128, 3).tolist(), "right": np.random.rand(128, 128, 3).tolist() }, "lidar": { "points": np.random.randn(1000, 3).tolist() # 1000个三维点云 }, "radar": { "frontal_objects": [ { "distance": random.uniform(5.0, 150.0), "speed": random.uniform(-10.0, 30.0), "angle": random.uniform(-30.0, 30.0) } for _ in range(random.randint(0, 3)) ] } } # 根据场景调整传感器数据 if scenario["event"] == "行人横穿": sensor["radar"]["frontal_objects"].append({ "distance": scenario["distance"], "speed": 1.5, # 行人步行速度 "angle": random.uniform(-15, 15) }) return sensor def generate_control_command(scenario): """生成控制指令""" base_speed = scenario["speed"] cmd_template = { "normal": "保持当前车速{}km/h，车道居中", "obstacle": { "行人横穿": "减速至{}km/h，准备制动", "车辆加塞": "减速至{}km/h，保持安全距离", "动物闯入": "鸣笛警示，减速至{}km/h" }, "traffic_control": { "红灯": "在距离{}米处开始减速，平稳停车", "施工路段": "减速至{}km/h，向右变道", "交警指挥": "按指挥手势行驶，保持车速{}km/h" }, "emergency": { "爆胎": "紧握方向盘，缓踩刹车，车速降至{}km/h", "刹车失灵": "启用电子手刹，车速降至{}km/h", "前方事故": "紧急制动，车速降至{}km/h" } } if scenario["event"] == "保持车道行驶": return cmd_template["normal"].format(base_speed) for category in ["obstacle", "traffic_control", "emergency"]: if scenario["event"] in cmd_template[category]: target_speed = max(base_speed * 0.5, 20) if category == "emergency" else base_speed * 0.7 return cmd_template[category][scenario["event"]].format(int(target_speed)) return "维持当前操作" def generate_dataset(num_samples=1000): """生成完整数据集""" dataset = [] scene_types = ["normal", "obstacle", "traffic_control", "emergency"] for _ in tqdm(range(num_samples)): scene_type = random.choices( scene_types, weights=[0.4, 0.3, 0.2, 0.1], # 场景类型分布 k=1 )[0] scenario = generate_scenario(scene_type) sensor_data = generate_sensor_data(scenario) command = generate_control_command(scenario) # 构建输入描述 input_desc = ( f"当前环境：{scenario['weather']}，{scenario['road_type']}，" f"车速{scenario['speed']}km/h。" ) if "distance" in scenario: input_desc += f"检测到{scenario['distance']}米处{scenario['event']}" # 构建数据样本 sample = { "input": input_desc, "output": command, "sensor_data": sensor_data, "metadata": { "scene_type": scene_type, "timestamp": fake.date_time_this_year().isoformat(), "location": fake.city() + "模拟道路" } } dataset.append(sample) return dataset # 生成并保存数据集 if __name__ == "__main__": dataset = generate_dataset(num_samples=5000) # 分割训练验证集 random.shuffle(dataset) split_idx = int(len(dataset)*0.9) with open("data/train.json", "w") as f: json.dump(dataset[:split_idx], f, ensure_ascii=False, indent=2) with open("data/val.json", "w") as f: json.dump(dataset[split_idx:], f, ensure_ascii=False, indent=2) print(f"数据集生成完成，共{len(dataset)}条样本（训练集：{split_idx}，验证集：{len(dataset)-split_idx}）") 数据集结构说明 输入描述示例： { "input": "当前环境：小雨，城市道路，车速45km/h。检测到28米处行人横穿", "output": "减速至32km/h，准备制动", "sensor_data": { "camera": {...}, "lidar": {...}, "radar": { "frontal_objects": [ {"distance": 28.3, "speed": 1.5, "angle": 3.2} ] } }, "metadata": { "scene_type": "obstacle", "timestamp": "2024-03-15T14:32:15", "location": "上海模拟道路" } }

场景覆盖范围：

天气条件：4种道路类型：4种障碍物类型：3种交通管制：3种紧急情况：3种

传感器数据模拟：

摄像头：模拟生成前视/左右摄像头图像（随机噪声）激光雷达：生成1000个三维点云毫米波雷达：生成前方物体距离/速度/角度 数据增强建议 真实传感器融合： # 使用CARLA仿真平台获取真实传感器数据 from carla import World, Sensor def capture_real_sensor_data(): world = connect_to_carla() camera = CameraSensor(world) lidar = LidarSensor(world) return { "camera": camera.capture(), "lidar": lidar.get_point_cloud() } 物理引擎增强： def add_physics_noise(data): # 为传感器数据添加物理合理的噪声 noise_levels = { "radar_distance": 0.1, # 10%距离噪声 "camera_brightness": 0.05 } if "radar" in data: for obj in data["radar"]["frontal_objects"]: obj["distance"] *= 1 + random.uniform(-noise_levels["radar_distance"], noise_levels["radar_distance"]) if "camera" in data: for cam in data["camera"].values(): cam = np.array(cam) cam += np.random.normal(0, noise_levels["camera_brightness"], cam.shape) return data 对抗样本生成： def generate_adversarial_samples(): # 生成极端情况样本 return [ { "input": "当前环境：暴雨，高速公路，车速120km/h。检测到5米处多车连环追尾", "output": "紧急制动！开启双闪！车速降至30km/h" }, { "input": "传感器故障：摄像头失效，雷达信号丢失", "output": "启用冗余系统，维持最低安全车速40km/h" } ] 数据集验证 统计分析脚本： from collections import Counter def analyze_dataset(dataset): scene_types = [s["metadata"]["scene_type"] for s in dataset] print("场景类型分布:", Counter(scene_types)) cmd_lengths = [len(s["output"]) for s in dataset] print(f"指令平均长度: {np.mean(cmd_lengths):.1f}字符") speed_changes = [int("减速" in s["output"]) for s in dataset] print(f"需要减速的场景占比: {np.mean(speed_changes)*100:.1f}%") analyze_dataset(dataset) 可视化检查： import matplotlib.pyplot as plt def visualize_sample(sample): plt.figure(figsize=(12, 6)) # 显示模拟摄像头图像 plt.subplot(1, 2, 1) plt.imshow(np.array(sample["sensor_data"]["camera"]["front"])) plt.title("前视摄像头模拟") # 显示雷达数据 plt.subplot(1, 2, 2) distances = [obj["distance"] for obj in sample["sensor_data"]["radar"]["frontal_objects"]] angles = [obj["angle"] for obj in sample["sensor_data"]["radar"]["frontal_objects"]] plt.scatter(angles, distances) plt.title("雷达探测示意图") plt.suptitle(f"控制指令: {sample['output']}") plt.show() visualize_sample(dataset[0])

该方案生成的合成数据集可用于：

自动驾驶决策模型的监督学习强化学习的环境模拟传感器融合算法的开发验证异常情况处理能力的压力测试

实际应用时建议：

逐步替换合成数据为真实道路采集数据添加车辆动力学参数（如转向角、加速度等）结合高精度地图信息增强场景语义增加驾驶员监控系统（DMS）的生理信号数据