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Explain hardcoded values (#12)
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* Added explanatory comments; Removed unused function

Signed-off-by: ClemensLinnhoff <[email protected]>

* Increase allowed cognitive complexity of functions

Signed-off-by: ClemensLinnhoff <[email protected]>

* Put rain and spray parameters to profile

Signed-off-by: ClemensLinnhoff <[email protected]>

---------

Signed-off-by: ClemensLinnhoff <[email protected]>
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@ClemensLinnhoff
ClemensLinnhoff committed May 8, 2023
1 parent 79b739d commit 35082e3
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Showing 10 changed files with 79 additions and 64 deletions.
2 changes: 2 additions & 0 deletions .clang-tidy
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Expand Up @@ -263,5 +263,7 @@ CheckOptions:
value: 'x|y|z|id|mu|a|b|c'
- key: readability-identifier-length.IgnoredParameterNames
value: 'x|y|z|id'
- key: readability-function-cognitive-complexity.Threshold
value: '50'
...

3 changes: 2 additions & 1 deletion src/model/strategies/lidar-detection-sensing-strategy/profile_VLP16.hpp.in
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Expand Up @@ -18,4 +18,5 @@
VLP16.detection_sensing_parameters.pulse_duration_stddev = 0.5; // Standard deviation of the pulse duration of the emitted laser pulse in ns
VLP16.detection_sensing_parameters.echo_determination_mode = "start"; // Determines what part of the echo pulse is taken for distance. Options: {"start" (default), "peak"}
VLP16.detection_sensing_parameters.intensity_or_epw = 0; // 0: Detections with intensity in %, 1: Detections with echo pulse width (epw) in m
VLP16.detection_sensing_parameters.range_compensate_intensity = true; // Compensate intensity by range with a factor of range^4
VLP16.detection_sensing_parameters.range_compensate_intensity = true; // Compensate intensity by range with a factor of range^4
VLP16.detection_sensing_parameters.thres_distance_m = 30.0; // Intensity range compensation starts at this distance
1 change: 1 addition & 0 deletions src/model/strategies/lidar-detection-sensing-strategy/profile_VLP32.hpp.in
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Expand Up @@ -18,3 +18,4 @@
VLP32.detection_sensing_parameters.echo_determination_mode = "start"; // Determines what part of the echo pulse is taken for distance. Options: {"start" (default), "peak"}
VLP32.detection_sensing_parameters.intensity_or_epw = 0; // 0: Detections with intensity in %, 1: Detections with echo pulse width (epw) in m
VLP32.detection_sensing_parameters.range_compensate_intensity = true; // Compensate intensity by range with a factor of range^4
VLP32.detection_sensing_parameters.thres_distance_m = 30.0; // Intensity range compensation starts at this distance
1 change: 1 addition & 0 deletions src/model/strategies/lidar-detection-sensing-strategy/profile_struct.hpp.in
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Expand Up @@ -19,6 +19,7 @@
float intensity_resolution; // Specified intensity resolution of the sensor output in %
float intensity_stddev; // Specified intensity standard deviation in %
bool range_compensate_intensity; // Compensate intensity by range with a factor of range^4
float thres_distance_m; // Intensity range compensation starts at this distance
std::vector<std::vector<float>> signal_strength_to_epw; // Lookup table for linear interpolation of epw part that correlates with signal strength in dBm

} detection_sensing_parameters;
5 changes: 2 additions & 3 deletions src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp
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Expand Up @@ -392,13 +392,12 @@ void DetectionSensing::threshold_summed_beam_cell(LidarBeamCellmW* summed_dist_c
{
auto summed_signal_strength_in_dBm = 10 * std::log10(summed_dist_cell_of_beam_ptr->signal_strength_in_mW);
double threshold = profile.detection_sensing_parameters.signal_strength_threshold_in_dBm;
double thres_distance_m = 30.0; // todo: put to profile
if (profile.detection_sensing_parameters.range_comp_threshold)
{
float range = profile.min_range + ((float)summed_dist_cell_of_beam_ptr->dist_cell_idx + (float)0.5) * profile.detection_sensing_parameters.distance_resolution_adc;
if (range > thres_distance_m)
if (range > profile.detection_sensing_parameters.thres_distance_m)
{
double threshold_mW = pow(10.0, threshold / 10.0) * pow(thres_distance_m, 2) / pow(range, 2);
double threshold_mW = pow(10.0, threshold / 10.0) * pow(profile.detection_sensing_parameters.thres_distance_m, 2) / pow(range, 2);
threshold = 10 * std::log10(threshold_mW);
}
}
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1 change: 0 additions & 1 deletion ...-effects-strategy/include/detectionenvironmentaleffects/DetectionEnvironmentalEffects.hpp
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Expand Up @@ -71,7 +71,6 @@ class DetectionEnvironmentalEffects : public Strategy
osi3::LidarDetectionData* current_sensor,
const osi3::SpatialSignalStrength& current_beam);
static void get_min_max_azimuth_of_cluster(SprayCluster& cluster, const osi3::MountingPosition& mounting_pose, const TF::EgoData& ego_data);
static bool check_if_sensor_in_cluster_volume(SprayCluster& cluster, const osi3::MountingPosition& mounting_pose, const TF::EgoData& ego_data);
static std::vector<double> intersection_with_sphere(const osi3::Vector3d& center, float radius, double azimuth, double elevation);

void simulate_wet_pavement(osi3::SensorData& sensor_data, const TF::EgoData& ego_data, LidarDetection& existing_detection, int sensor_idx, double water_film_height);
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7 changes: 5 additions & 2 deletions src/model/strategies/lidar-environmental-effects-strategy/profile_VLP16.hpp.in
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Expand Up @@ -9,7 +9,10 @@
VLP16.det_envir_effects.fog_det_prob_factor = 0; // factor for linear function with meteorological visibility

VLP16.det_envir_effects.calibrated_rain = true; // does a parameter set for rain calibrated from to world measurements exist?
VLP16.det_envir_effects.rain_det_prob_factor = 0.000169; // factor for linear function with precipitation rate
VLP16.det_envir_effects.rain_det_prob_factor = 0.000169; // factor for linear function with precipitation rate
VLP16.det_envir_effects.rain_detection_dist_distr_mu = 1.362; // mean value for distance distribution of rain detections
VLP16.det_envir_effects.rain_detection_dist_distr_sigma = 0.784;// standard deviation for distance distribution of rain detections
VLP16.det_envir_effects.rain_attenuation_factor = 7.677; // factor for rain intensity dependent signal attenuation calculated by rain_intensity * rain_attenuation_factor * pow(10, -5

VLP16.det_envir_effects.calibrated_snow = true; // does a parameter set for snow calibrated from to world measurements exist?
VLP16.det_envir_effects.snow_det_prob_factor = 0.01569; // factor for linear function with precipitation rate
Expand All @@ -21,5 +24,5 @@
VLP16.det_envir_effects.distance_distr_sigma = 0.7868; // standard deviation of log-normal distance distribution
VLP16.det_envir_effects.intensity_distr_lambda = 0.69312; // mean value of poisson intensity distribution

//spray
//spray
VLP16.det_envir_effects.calibrated_spray = false; // does a parameter set for spray calibrated to real world measurements exist?
23 changes: 17 additions & 6 deletions src/model/strategies/lidar-environmental-effects-strategy/profile_VLP32.hpp.in
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Expand Up @@ -5,14 +5,16 @@
/// detection_environmental_effects_parameters

//weather
VLP32.det_envir_effects.calibrated_fog = false; // does a parameter set for fog calibrated from to world measurements exist?
VLP32.det_envir_effects.calibrated_fog = false; // does a parameter set for fog calibrated from to world measurements exist?
VLP32.det_envir_effects.fog_det_prob_factor = 0; // factor for linear function with meteorological visibility

VLP32.det_envir_effects.calibrated_rain = false; // does a parameter set for rain calibrated from to world measurements exist?
VLP32.det_envir_effects.rain_det_prob_factor = 0.000169; // factor for linear function with precipitation rate
VLP32.det_envir_effects.calibrated_rain = false; // does a parameter set for rain calibrated from to world measurements exist?
VLP32.det_envir_effects.rain_det_prob_factor = 0.000169; // factor for linear function with precipitation rate
VLP32.det_envir_effects.rain_detection_dist_distr_mu = 1.362; // mean value for distance distribution of rain detections
VLP32.det_envir_effects.rain_detection_dist_distr_sigma = 0.784;// standard deviation for distance distribution of rain detections

VLP32.det_envir_effects.calibrated_snow = false; // does a parameter set for snow calibrated from to world measurements exist?
VLP32.det_envir_effects.snow_det_prob_factor = 0; // factor for linear function with precipitation rate
VLP32.det_envir_effects.calibrated_snow = false; // does a parameter set for snow calibrated from to world measurements exist?
VLP32.det_envir_effects.snow_det_prob_factor = 0; // factor for linear function with precipitation rate

VLP32.det_envir_effects.calibrated_sun = false; // does a parameter set for direct sun light calibrated to real world measurements exist?

Expand All @@ -22,4 +24,13 @@
VLP32.det_envir_effects.intensity_distr_lambda = 0.69312; // mean value of poisson intensity distribution

//spray
VLP32.det_envir_effects.calibrated_spray = true; // does a parameter set for spray calibrated to real world measurements exist?
VLP32.det_envir_effects.calibrated_spray = true; // does a parameter set for spray calibrated to real world measurements exist?
VLP32.det_envir_effects.distance_distr_in_cluster_mu = -2.3; // log mean value for distance distribution of detections in a spray cluster
VLP32.det_envir_effects.distance_distr_in_cluster_sigma = 1.1; // log standard deviation for distance distribution of detections in a spray cluster
VLP32.det_envir_effects.mean_attenuation_in_cluster = 0.02; // mean attenuation in spray cluster used for attenuation_factor = exp(-2.0 * mean_attenuation_in_cluster * distance_in_spray_cluster);
VLP32.det_envir_effects.std_attenuation_in_cluster = (0.285 * pow(10, -6)); // standard deviation for attenuation of existing detections due to spray
VLP32.det_envir_effects.num_clusters_wfh_factor = 0.2; // mean_num_clusters = (num_clusters_wfh_factor * water_film_height + num_clusters_wfh_offset) * (object_velocity * 3.6 - num_clusters_velocity_offset);
VLP32.det_envir_effects.num_clusters_wfh_offset = 0.1; // mean_num_clusters = (num_clusters_wfh_factor * water_film_height + num_clusters_wfh_offset) * (object_velocity * 3.6 - num_clusters_velocity_offset);
VLP32.det_envir_effects.num_clusters_velocity_offset_kmh = 50.0;// mean_num_clusters = (num_clusters_wfh_factor * water_film_height + num_clusters_wfh_offset) * (object_velocity * 3.6 - num_clusters_velocity_offset);
VLP32.det_envir_effects.cluster_radius_dist_mu = -1.2; // log mean value for radius distribution of spray cluster
VLP32.det_envir_effects.cluster_radius_dist_sigma = 0.8; // log standard deviation for radius distribution of spray cluster
13 changes: 12 additions & 1 deletion src/model/strategies/lidar-environmental-effects-strategy/profile_struct.hpp.in
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Expand Up @@ -10,6 +10,9 @@

bool calibrated_rain; // does a parameter set for rain calibrated from to world measurements exist?
float rain_det_prob_factor; // factor for linear function with precipitation rate
double rain_detection_dist_distr_mu; // mean value for distance distribution of rain detections
double rain_detection_dist_distr_sigma; // standard deviation for distance distribution of rain detections
double rain_attenuation_factor; // factor for rain intensity dependent signal attenuation calculated by rain_intensity * rain_attenuation_factor * pow(10, -5)

bool calibrated_snow; // does a parameter set for snow calibrated from to world measurements exist?
float snow_det_prob_factor; // factor for linear function with precipitation rate
Expand All @@ -23,6 +26,14 @@

//spray
bool calibrated_spray; // does a parameter set for spray calibrated to real world measurements exist?

float distance_distr_in_cluster_mu; // log mean value for distance distribution of detections in a spray cluster
float distance_distr_in_cluster_sigma; // log standard deviation for distance distribution of detections in a spray cluster
float mean_attenuation_in_cluster; // mean attenuation in spray cluster used for attenuation_factor = exp(-2.0 * mean_attenuation_in_cluster * distance_in_spray_cluster);
float std_attenuation_in_cluster; // standard deviation for attenuation of existing detections due to spray
double num_clusters_wfh_factor; // mean_num_clusters = (num_clusters_wfh_factor * water_film_height + num_clusters_wfh_offset) * (object_velocity * 3.6 - num_clusters_velocity_offset);
double num_clusters_wfh_offset; // mean_num_clusters = (num_clusters_wfh_factor * water_film_height + num_clusters_wfh_offset) * (object_velocity * 3.6 - num_clusters_velocity_offset);
double num_clusters_velocity_offset_kmh;// mean_num_clusters = (num_clusters_wfh_factor * water_film_height + num_clusters_wfh_offset) * (object_velocity * 3.6 - num_clusters_velocity_offset);
float cluster_radius_dist_mu; // log mean value for radius distribution of spray cluster
float cluster_radius_dist_sigma; // log standard deviation for radius distribution of spray cluster

} det_envir_effects;
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💬 Output from clang-tidy

src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp
/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:124:22: warning: [readability-identifier-naming]

invalid case style for local variable 'signal_strength_in_dBm'

                auto signal_strength_in_dBm = rendering_result.received_signal().signal_strength();
                     ^~~~~~~~~~~~~~~~~~~~~~
                     signal_strength_in_d_bm

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:176:24: warning: [readability-function-cognitive-complexity]

function 'process_collected_beam_cells' has cognitive complexity of 52 (threshold 50)

void DetectionSensing::process_collected_beam_cells(LidarDetectionData* current_sensor,
                       ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:187:15: note: nesting level increased to 1
              [](const DetectionSensing::LidarBeamCellmW& first, const DetectionSensing::LidarBeamCellmW& second) { return first.dist_cell_idx < second.dist_cell_idx; });
              ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:195:5: note: +1, including nesting penalty of 0, nesting level increased to 1
    for (auto& lidar_cuboid_cell_of_beam : lidar_cuboid_cells_of_beam)
    ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:197:9: note: +2, including nesting penalty of 1, nesting level increased to 2
        if (lidar_cuboid_cell_of_beam.dist_cell_idx == summed_dist_cell_of_beam.dist_cell_idx)
        ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:201:9: note: +1, nesting level increased to 2
        else
        ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:210:5: note: +1, including nesting penalty of 0, nesting level increased to 1
    if (!thresholded_summed_dist_cells.empty())
    ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:227:9: note: +2, including nesting penalty of 1, nesting level increased to 2
        for (size_t thresholded_summed_dist_cell_dB_idx = 1; thresholded_summed_dist_cell_dB_idx < thresholded_summed_dist_cells.size(); thresholded_summed_dist_cell_dB_idx++)
        ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:230:13: note: +3, including nesting penalty of 2, nesting level increased to 3
            if ((float)thresholded_summed_dist_cells[thresholded_summed_dist_cell_dB_idx].dist_cell_idx >
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:236:17: note: +4, including nesting penalty of 3, nesting level increased to 4
                if (no_of_peaks_in_beam > profile.detection_sensing_parameters.max_echos_per_beam)
                ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:252:13: note: +1, nesting level increased to 3
            else
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:258:17: note: +4, including nesting penalty of 3, nesting level increased to 4
                if (thresholded_summed_dist_cells[thresholded_summed_dist_cell_dB_idx].signal_strength_in_dBm > peaks_in_beam[no_of_peaks_in_beam - 1].signal_strength_in_dBm)
                ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:269:9: note: +2, including nesting penalty of 1, nesting level increased to 2
        for (auto& current_peak : peaks_in_beam)
        ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:275:13: note: +3, including nesting penalty of 2, nesting level increased to 3
            if (profile.detection_sensing_parameters.echo_determination_mode == "peak")
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:279:13: note: +1, nesting level increased to 3
            else
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:287:13: note: +3, including nesting penalty of 2, nesting level increased to 3
            if ((profile.vertical_angle_clamping != "center") && (profile.beam_step_elevation > 0))
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:287:63: note: +1
            if ((profile.vertical_angle_clamping != "center") && (profile.beam_step_elevation > 0))
                                                              ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:289:17: note: +4, including nesting penalty of 3, nesting level increased to 4
                if (profile.vertical_angle_clamping == "max_abs")
                ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:294:22: note: +1, nesting level increased to 4
                else if (profile.vertical_angle_clamping == "top")
                     ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:298:22: note: +1, nesting level increased to 4
                else if (profile.vertical_angle_clamping == "bottom")
                     ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:305:13: note: +3, including nesting penalty of 2, nesting level increased to 3
            if (profile.detection_sensing_parameters.distance_stddev > 0.0)
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:321:13: note: +3, including nesting penalty of 2, nesting level increased to 3
            if (profile.detection_sensing_parameters.intensity_or_epw == 0)
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:324:17: note: +4, including nesting penalty of 3, nesting level increased to 4
                if (profile.detection_sensing_parameters.range_compensate_intensity)
                ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:335:17: note: +1, nesting level increased to 4
                else
                ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:358:13: note: +1, nesting level increased to 3
            else
            ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:373:17: note: +4, including nesting penalty of 3, nesting level increased to 4
                while ((count < signal_strength_to_epw.size() - 1) &&
                ^
/home/runner/work/sl-1-2-reflection-based-lidar-object-model/sl-1-2-reflection-based-lidar-object-model/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:373:68: note: +1
                while ((count < signal_strength_to_epw.size() - 1) &&
                                                                   ^

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:227:21: warning: [readability-identifier-naming]

invalid case style for local variable 'thresholded_summed_dist_cell_dB_idx'

        for (size_t thresholded_summed_dist_cell_dB_idx = 1; thresholded_summed_dist_cell_dB_idx < thresholded_summed_dist_cells.size(); thresholded_summed_dist_cell_dB_idx++)
                    ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~                                         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
                    thresholded_summed_dist_cell_d_b_idx     thresholded_summed_dist_cell_d_b_idx                                        thresholded_summed_dist_cell_d_b_idx

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:326:28: warning: [readability-identifier-naming]

invalid case style for local variable 'signal_strength_in_mW'

                    double signal_strength_in_mW = pow(10, current_peak.signal_strength_in_dBm / 10);
                           ^~~~~~~~~~~~~~~~~~~~~
                           signal_strength_in_m_w

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:327:28: warning: [readability-identifier-naming]

invalid case style for local variable 'signal_strength_in_mW_range_compensated'

                    double signal_strength_in_mW_range_compensated = signal_strength_in_mW * (pow(detection->position().distance(), 2));
                           ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
                           signal_strength_in_m_w_range_compensated

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:329:28: warning: [readability-identifier-naming]

invalid case style for local variable 'emitted_signal_strength_mW'

                    double emitted_signal_strength_mW = pow(10.0, profile.max_emitted_signal_strength_in_dBm / 10.0);
                           ^~~~~~~~~~~~~~~~~~~~~~~~~~
                           emitted_signal_strength_m_w

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:393:10: warning: [readability-identifier-naming]

invalid case style for local variable 'summed_signal_strength_in_dBm'

    auto summed_signal_strength_in_dBm = 10 * std::log10(summed_dist_cell_of_beam_ptr->signal_strength_in_mW);
         ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         summed_signal_strength_in_d_bm

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:400:20: warning: [readability-identifier-naming]

invalid case style for local variable 'threshold_mW'

            double threshold_mW = pow(10.0, threshold / 10.0) * pow(profile.detection_sensing_parameters.thres_distance_m, 2) / pow(range, 2);
                   ^~~~~~~~~~~~
                   threshold_m_w

/src/model/strategies/lidar-detection-sensing-strategy/src/DetectionSensing.cpp:407:26: warning: [readability-identifier-naming]

invalid case style for local variable 'thresholded_summed_dist_cell_dBm'

        LidarBeamCelldBm thresholded_summed_dist_cell_dBm;
                         ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
                         thresholded_summed_dist_cell_d_bm

src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp
/src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp:338:26: warning: [readability-identifier-naming]

invalid case style for local constant 'Gamma'

            const double Gamma = existing_detection.intensity() / 100.0 * 255.0 / 100.0;  // todo: calibrated to intensity values of Velodyne sensor. Change for other lidar types.
                         ^~~~~
                         gamma

/src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp:748:20: warning: [readability-identifier-naming]

invalid case style for local variable 'emitted_signal_strength_mW'

            double emitted_signal_strength_mW = pow(10.0, profile.max_emitted_signal_strength_in_dBm / 10.0);
                   ^~~~~~~~~~~~~~~~~~~~~~~~~~
                   emitted_signal_strength_m_w

/src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp:750:20: warning: [readability-identifier-naming]

invalid case style for local variable 'attenuated_power_mW'

            double attenuated_power_mW =
                   ^~~~~~~~~~~~~~~~~~~
                   attenuated_power_m_w

/src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp:755:20: warning: [readability-identifier-naming]

invalid case style for local variable 'attenuated_power_noise_mW'

            double attenuated_power_noise_mW = noise_distribution(generator);
                   ^~~~~~~~~~~~~~~~~~~~~~~~~
                   attenuated_power_noise_m_w

/src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp:756:20: warning: [readability-identifier-naming]

invalid case style for local variable 'attenuated_power_dBm'

            double attenuated_power_dBm = 10 * log10(attenuated_power_noise_mW);
                   ^~~~~~~~~~~~~~~~~~~~
                   attenuated_power_d_bm

/src/model/strategies/lidar-environmental-effects-strategy/src/DetectionEnvironmentalEffects.cpp:763:28: warning: [readability-identifier-naming]

invalid case style for local variable 'threshold_mW'

                    double threshold_mW = pow(10.0, threshold / 10.0) * pow(profile.detection_sensing_parameters.thres_distance_m, 2) / pow(range, 2);
                           ^~~~~~~~~~~~
                           threshold_m_w

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