NVIDIA 智能汽车驾驶技术解析：：从概念到实践

def is_anomalous(self):
        """
        检测异常驾驶行为
        """
        features = self.calculate_features()
        if features is None:
            return False, "数据不足"
            
        anomalies = []
        
        # 检测速度异常
        if features['speed_variance'] > self.normal_speed_variance * 3:
            anomalies.append(f"速度不稳定 (方差: {features['speed_variance']:.2f})")
            
        # 检测转向异常
        if features['steering_frequency'] > self.normal_steering_frequency * 3:
            anomalies.append(f"转向过于频繁 ({features['steering_frequency']}次/秒)")
            
        # 检测急加速/急减速
        if len(self.speed_history) >= 2:
            acceleration = self.speed_history[-1] - self.speed_history[-2]
            if abs(acceleration) > 20:
                anomalies.append(f"急加速/急减速 (加速度: {acceleration:.2f} km/h/s)")
        
        if anomalies:
            return True, "; ".join(anomalies)
        else:
            return False, "驾驶行为正常"
    
    def get_safety_score(self):
        """
        计算驾驶安全评分
        """
        features = self.calculate_features()
        if features is None:
            return 100  # 默认满分
            
        # 基于特征计算安全评分
        score = 100
        
        # 速度稳定性评分
        if features['speed_variance'] > self.normal_speed_variance:
            score -= min(30, (features['speed_variance'] / self.normal_speed_variance) * 10)
            
        # 转向稳定性评分
        if features['steering_frequency'] > self.normal_steering_frequency:
            score -= min(30, (features['steering_frequency'] / self.normal_steering_frequency) * 10)
            
        return max(0, score)  # 确保分数不低于0

def get_device_info(self):
        """
        获取设备信息
        """
        return {
            "device_id": "NVIDIA_DEMO_001",
            "current_version": self.current_version,
            "os_version": " DRIVE OS 11.0",
            "hardware": "DRIVE AGX Orin DevKit"
        }
    
    def check_for_updates(self):
        """
        检查是否有可用更新
        """
        try:
            # 发送设备信息到更新服务器
            device_info = self.get_device_info()
            response = requests.post(f"{self.update_server}/api/check_update", 
                                   json=device_info, timeout=10)
            
            if response.status_code == 200:
                update_info = response.json()
                
                # 比较版本号
                if self._compare_versions(update_info.get("version", "0.0.0"), 
                                        self.current_version) > 0:
                    self.update_available = True
                    self.update_info = update_info
                    return True, update_info
                else:
                    self.update_available = False
                    return False, "当前已是最新版本"
            else:
                return False, f"检查更新失败: HTTP {response.status_code}"
                
        except requests.exceptions.RequestException as e:
            return False, f"网络错误: {str(e)}"
        except Exception as e:
            return False, f"检查更新时发生错误: {str(e)}"

def __init__(self):
        # 传感器权重
        self.camera_weight = 0.4
        self.lidar_weight = 0.4
        self.radar_weight = 0.2
        
    def process_camera(self, image):
        """
        处理摄像头数据
        检测车道线和前方车辆
        """
        # 转换为灰度图
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # 边缘检测
        edges = cv2.Canny(gray, 50, 150)
        
        # 简单的车道线检测
        height, width = edges.shape
        roi = edges[int(height/2):, :]
        
        # 计算车道线置信度
        lane_pixels = np.count_nonzero(roi)
        total_pixels = roi.size
        lane_confidence = lane_pixels / total_pixels if total_pixels > 0 else 0
        
        return {
            'lane_confidence': lane_confidence,
            'lane_detected': lane_confidence > 0.05
        }

def average_slope_intercept(self, image, lines):
        """
        平均斜率和截距以获得左右车道线
        """
        left_fit = []
        right_fit = []
        if lines is not None:
            for line in lines:
                x1, y1, x2, y2 = line.reshape(4)
                parameters = np.polyfit((x1, x2), (y1, y2), 1)
                slope = parameters[0]
                intercept = parameters[1]
                if slope < 0:
                    left_fit.append((slope, intercept))
                else:
                    right_fit.append((slope, intercept))
                    
            left_fit_average = np.average(left_fit, axis=0)
            right_fit_average = np.average(right_fit, axis=0)
            
            left_line = self.make_coordinates(image, left_fit_average)
            right_line = self.make_coordinates(image, right_fit_average)
            
            return np.array([left_line, right_line])