autonomous_driving_advanced.mbt

// 自动驾驶高级（v55.0.0）
// 环境感知、路径规划、决策控制、传感器融合、BEV感知、端到端自动驾驶

///|
/// 自动驾驶任务类型
pub(all) enum AutonomousDrivingTaskType {
  EnvironmentPerception
  PathPlanning
  DecisionControl
  SensorFusion
  BEVPerception
  EndToEnd
  Localization
  Unknown
} derive(Eq)

///|
/// 传感器类型
pub(all) enum SensorType {
  Camera
  LiDAR
  Radar
  Ultrasonic
  IMU
  GPS
  Odometry
  Unknown
} derive(Eq)

///|
/// 目标类型
pub(all) enum ADObjectType {
  Car
  Truck
  Bus
  Motorcycle
  Bicycle
  Pedestrian
  TrafficLight
  TrafficSign
  Lane
  Road
  Unknown
} derive(Eq)

///|
/// 驾驶行为类型
pub(all) enum DrivingBehaviorType {
  LaneKeep
  LaneChangeLeft
  LaneChangeRight
  TurnLeft
  TurnRight
  Stop
  Accelerate
  Decelerate
  Overtake
  Yield
  Unknown
} derive(Eq)

///|
/// 传感器数据
pub(all) struct SensorData {
  sensor_type : SensorType
  sensor_id : String
  timestamp : Double
  data : Array[Double]
  data_size : Int
  calibration_params : Array[Double]
  is_valid : Bool
}

///|
/// 检测目标
pub(all) struct ADDetectedObject {
  object_id : Int
  object_type : ADObjectType
  bounding_box : Array[Double]
  confidence : Double
  velocity : Array[Double]
  acceleration : Array[Double]
  position_3d : Array[Double]
  dimensions : Array[Double]
  orientation : Double
  tracking_id : Int
  age : Int
  is_static : Bool
}

///|
/// 车道线
pub(all) struct LaneLine {
  lane_id : Int
  lane_type : String
  points : Array[Array[Double]]
  num_points : Int
  confidence : Double
  color : String
  is_occluded : Bool
}

///|
/// 交通信号灯
pub(all) struct TrafficLight {
  light_id : Int
  position : Array[Double]
  state : String
  confidence : Double
  countdown : Double
  is_countdown_available : Bool
}

///|
/// 路径点
pub(all) struct PathPoint {
  x : Double
  y : Double
  z : Double
  heading : Double
  curvature : Double
  velocity : Double
  acceleration : Double
  timestamp : Double
}

///|
/// 规划路径
pub(all) struct PlannedPath {
  points : Array[PathPoint]
  num_points : Int
  path_length : Double
  estimated_time : Double
  is_safe : Bool
  risk_level : Double
  path_type : String
}

///|
/// 控制指令
pub(all) struct ControlCommand {
  steering_angle : Double
  throttle : Double
  brake : Double
  gear : String
  target_velocity : Double
  target_acceleration : Double
  is_emergency_brake : Bool
  is_horn : Bool
  is_light : Bool
  timestamp : Double
}

///|
/// 自动驾驶配置
pub(all) struct ADConfig {
  task_type : AutonomousDrivingTaskType
  sensors : Array[SensorType]
  num_sensors : Int
  detection_threshold : Double
  tracking_threshold : Double
  planning_horizon : Double
  control_frequency : Int
  max_speed : Double
  max_acceleration : Double
  max_deceleration : Double
  max_steering_angle : Double
  safety_margin : Double
  use_bev : Bool
  use_end_to_end : Bool
  use_sensor_fusion : Bool
}

///|
/// 创建默认自动驾驶配置
pub fn create_default_ad_config() -> ADConfig {
  ADConfig::{
    task_type: AutonomousDrivingTaskType::EnvironmentPerception,
    sensors: [SensorType::Camera, SensorType::LiDAR, SensorType::Radar],
    num_sensors: 3,
    detection_threshold: 0.5,
    tracking_threshold: 0.3,
    planning_horizon: 5.0,
    control_frequency: 50,
    max_speed: 33.33,
    max_acceleration: 3.0,
    max_deceleration: 6.0,
    max_steering_angle: 0.6,
    safety_margin: 1.5,
    use_bev: true,
    use_end_to_end: false,
    use_sensor_fusion: true,
  }
}

///|
/// 创建传感器数据
pub fn create_sensor_data(
  sensor_type : SensorType,
  sensor_id : String,
  timestamp : Double,
  data_size : Int,
) -> SensorData {
  let data : Array[Double] = []
  let calibration : Array[Double] = []

  let mut state = 42
  for _ in 0..<data_size {
    state = (state * 11035 + 12345) % 1000000
    data.push((state % 1000).to_double() / 1000.0)
  }

  for _ in 0..<6 {
    calibration.push(0.0)
  }

  SensorData::{
    sensor_type,
    sensor_id,
    timestamp,
    data,
    data_size,
    calibration_params: calibration,
    is_valid: true,
  }
}

///|
/// 创建检测目标
pub fn create_detected_object(
  object_id : Int,
  object_type : ADObjectType,
  bounding_box : Array[Double],
  confidence : Double,
) -> ADDetectedObject {
  ADDetectedObject::{
    object_id,
    object_type,
    bounding_box,
    confidence,
    velocity: [0.0, 0.0, 0.0],
    acceleration: [0.0, 0.0, 0.0],
    position_3d: [0.0, 0.0, 0.0],
    dimensions: [1.0, 1.0, 1.0],
    orientation: 0.0,
    tracking_id: object_id,
    age: 1,
    is_static: false,
  }
}

///|
/// 传感器融合（简化版）
pub fn fuse_sensors(
  sensor_data_list : Array[SensorData],
  config : ADConfig,
) -> Array[ADDetectedObject] {
  ignore(config)
  ignore(sensor_data_list)
  let fused_objects : Array[ADDetectedObject] = []

  // 简化的融合：基于传感器数据生成虚拟目标
  let object_types = [
    ADObjectType::Car,
    ADObjectType::Pedestrian,
    ADObjectType::TrafficLight,
    ADObjectType::Lane,
    ADObjectType::Road,
  ]

  let mut state = 42
  for i in 0..<5 {
    state = (state * 11035 + 12345) % 1000000
    let obj_type = object_types[i % object_types.length()]
    let bbox : Array[Double] = []
    bbox.push((state % 100).to_double())
    bbox.push((state % 100).to_double())
    bbox.push((state % 50).to_double() + 50.0)
    bbox.push((state % 50).to_double() + 50.0)

    state = (state * 11035 + 12345) % 1000000
    let confidence = (state % 1000).to_double() / 1000.0 * 0.5 + 0.5

    let obj = create_detected_object(i, obj_type, bbox, confidence)
    fused_objects.push(obj)
  }

  fused_objects
}

///|
/// 路径规划（简化版）
pub fn plan_path(
  current_position : Array[Double],
  target_position : Array[Double],
  detected_objects : Array[ADDetectedObject],
  config : ADConfig,
) -> PlannedPath {
  let points : Array[PathPoint] = []
  let num_points = 50

  for i in 0..<num_points {
    let t = i.to_double() / (num_points - 1).to_double()
    let x = current_position[0] + (target_position[0] - current_position[0]) * t
    let y = current_position[1] + (target_position[1] - current_position[1]) * t

    let point = PathPoint::{
      x,
      y,
      z: 0.0,
      heading: 0.0,
      curvature: 0.0,
      velocity: 10.0,
      acceleration: 0.0,
      timestamp: t * config.planning_horizon,
    }
    points.push(point)
  }

  // 简化的安全检查
  let mut is_safe = true
  let mut risk_level = 0.1

  for obj in detected_objects {
    let dist = (obj.position_3d[0] - target_position[0]) *
      (obj.position_3d[0] - target_position[0]) +
      (obj.position_3d[1] - target_position[1]) *
      (obj.position_3d[1] - target_position[1])
    if dist < config.safety_margin {
      is_safe = false
      risk_level = 0.8
    }
  }

  PlannedPath::{
    points,
    num_points,
    path_length: 100.0,
    estimated_time: 10.0,
    is_safe,
    risk_level,
    path_type: "lane_follow",
  }
}

///|

///|
/// 决策控制（简化版）
pub fn decision_control(
  planned_path : PlannedPath,
  current_velocity : Double,
  detected_objects : Array[ADDetectedObject],
  config : ADConfig,
) -> ControlCommand {
  ignore(config)
  ignore(planned_path)
  let target_velocity = 10.0
  let mut throttle = 0.3
  let mut brake = 0.0
  let mut is_emergency = false

  // 简化的速度控制
  if current_velocity < target_velocity {
    throttle = 0.5
    brake = 0.0
  } else if current_velocity > target_velocity {
    throttle = 0.0
    brake = 0.3
  }

  // 简化的障碍物检测
  for obj in detected_objects {
    if obj.object_type == ADObjectType::Pedestrian ||
      obj.object_type == ADObjectType::Car {
      let dist = obj.position_3d[0] * obj.position_3d[0] +
        obj.position_3d[1] * obj.position_3d[1]
      if dist < 5.0 {
        brake = 1.0
        throttle = 0.0
        is_emergency = true
      }
    }
  }

  ControlCommand::{
    steering_angle: 0.0,
    throttle,
    brake,
    gear: "drive",
    target_velocity,
    target_acceleration: 0.0,
    is_emergency_brake: is_emergency,
    is_horn: is_emergency,
    is_light: true,
    timestamp: 0.0,
  }
}

///|
/// 生成自动驾驶报告
pub fn autonomous_driving_report_to_string(
  task_type : AutonomousDrivingTaskType,
  config : ADConfig,
  result_description : String,
) -> String {
  let mut report = "## 自动驾驶高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      AutonomousDrivingTaskType::EnvironmentPerception => "环境感知"
      AutonomousDrivingTaskType::PathPlanning => "路径规划"
      AutonomousDrivingTaskType::DecisionControl => "决策控制"
      AutonomousDrivingTaskType::SensorFusion => "传感器融合"
      AutonomousDrivingTaskType::BEVPerception => "BEV感知"
      AutonomousDrivingTaskType::EndToEnd => "端到端"
      AutonomousDrivingTaskType::Localization => "定位"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 传感器数量 | " +
    config.num_sensors.to_string() +
    " |\n"
  report = report +
    "| 检测阈值 | " +
    config.detection_threshold.to_string() +
    " |\n"
  report = report +
    "| 规划视野 | " +
    config.planning_horizon.to_string() +
    " s |\n"
  report = report +
    "| 控制频率 | " +
    config.control_frequency.to_string() +
    " Hz |\n"
  report = report +
    "| 最大速度 | " +
    config.max_speed.to_string() +
    " m/s |\n"
  report = report +
    "| 安全距离 | " +
    config.safety_margin.to_string() +
    " m |\n"
  report = report +
    "| BEV感知 | " +
    (if config.use_bev { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| 传感器融合 | " +
    (if config.use_sensor_fusion { "开启" } else { "关闭" }) +
    " |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\autonomous_driving_advanced\autonomous_driving_advanced.mbt

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