Calculate 2D Euclidean distance between track and detection. More...

Collaboration diagram for carma_cooperative_perception::SemanticDistance2dScore:

Public Member Functions
template<typename Track , typename Detection >
auto	operator() (const Track &track, const Detection &detection) const -> std::optional< float >

template<typename... TrackAlternatives, typename... DetectionAlternatives>
auto	operator() (const std::variant< TrackAlternatives... > &track, const std::variant< DetectionAlternatives... > &detection) const -> std::optional< float >

Static Private Member Functions
static auto	two_dimensional_distance (const mot::CtrvState &lhs, const mot::CtrvState &rhs) -> float

static auto	two_dimensional_distance (const mot::CtraState &lhs, const mot::CtraState &rhs) -> float

Detailed Description

Calculate 2D Euclidean distance between track and detection.

If both the track and detection semantic classes are known, this function object returns the 2D Euclidean distance between their states. Otherwise, it returns std::nullopt.

Definition at line 467 of file multiple_object_tracker_component.cpp.

Member Function Documentation

◆ operator()() [1/2]

template<typename... TrackAlternatives, typename... DetectionAlternatives>

auto carma_cooperative_perception::SemanticDistance2dScore::operator()	(	const std::variant< TrackAlternatives... > &	track,
		const std::variant< DetectionAlternatives... > &	detection
	)		const -> std::optional<float>

inline

Definition at line 492 of file multiple_object_tracker_component.cpp.

  {
    return std::visit(
      [this](const auto & t, const auto & d) { return (*this)(t, d); }, track, detection);
  }

◆ operator()() [2/2]

template<typename Track , typename Detection >

auto carma_cooperative_perception::SemanticDistance2dScore::operator()	(	const Track &	track,
		const Detection &	detection
	)		const -> std::optional<float>

inline

Definition at line 470 of file multiple_object_tracker_component.cpp.

  {
    if constexpr (std::is_same_v<decltype(track.state), decltype(detection.state)>) {
      const auto dist{two_dimensional_distance(track.state, detection.state)};
 
      // Fall back to 2D Euclidean distance if either semantic class if unknown. The unknown
      // track/detection may actually be the same other track/detection we are scoring against.
      if (
        track.semantic_class == mot::SemanticClass::kUnknown ||
        detection.semantic_class == mot::SemanticClass::kUnknown) {
        return dist;
      }
 
      if (track.semantic_class == detection.semantic_class) {
        return dist;
      }
    }
 
    return std::nullopt;
  }

◆ two_dimensional_distance() [1/2]

static auto carma_cooperative_perception::SemanticDistance2dScore::two_dimensional_distance	(	const mot::CtraState &	lhs,
		const mot::CtraState &	rhs
	)		-> float

inlinestaticprivate

Definition at line 512 of file multiple_object_tracker_component.cpp.

  {
    const auto x_diff_sq{
      std::pow(mot::remove_units(lhs.position_x) - mot::remove_units(rhs.position_x), 2)};
    const auto y_diff_sq{
      std::pow(mot::remove_units(lhs.position_y) - mot::remove_units(rhs.position_y), 2)};
 
    return std::sqrt(x_diff_sq + y_diff_sq);
  }

References carma_cooperative_perception::remove_units().

Here is the call graph for this function:

◆ two_dimensional_distance() [2/2]

static auto carma_cooperative_perception::SemanticDistance2dScore::two_dimensional_distance	(	const mot::CtrvState &	lhs,
		const mot::CtrvState &	rhs
	)		-> float

inlinestaticprivate

Definition at line 501 of file multiple_object_tracker_component.cpp.

  {
    const auto x_diff_sq{
      std::pow(mot::remove_units(lhs.position_x) - mot::remove_units(rhs.position_x), 2)};
    const auto y_diff_sq{
      std::pow(mot::remove_units(lhs.position_y) - mot::remove_units(rhs.position_y), 2)};
 
    return std::sqrt(x_diff_sq + y_diff_sq);
  }