-
Notifications
You must be signed in to change notification settings - Fork 13
/
trackingForFCW_kernel.m
508 lines (463 loc) · 22.4 KB
/
trackingForFCW_kernel.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
function [confirmedTracks, egoLane, numTracks, mostImportantObject] = ...
trackingForFCW_kernel(visionObjects, radarObjects, inertialMeasurementUnit, ...
laneReports, egoLane, time, positionSelector, velocitySelector)
% This function is for demo purposes only and may be removed in the future
% trackingForFCW_kernel A function that contains all the tracking related
% functions required for the
% <matlab:web(fullfile(docroot,'driving','examples','forward-collision-warning-using-vision-and-radar-sensors.html'));
% Forward Collision Warning Using Vision and Radar Sensors> example.
%
% Inputs:
% visionObjects - a struct containing 10 vision objects.
% radarObjects - a struct containing 36 radar objects.
% inertialMeasurementUnit - a struct with speed and yaw rate of ego car.
% laneReports - a struct with parameters of the left and right lane boundaries.
% egoLane - parameters of the left and right lane boundaries of the ego lane.
% time - the time to which the tracker is updated.
% positionSelector - a matrix that selects the position from the state vector
% velocitySelector - a matrix that selects the velocity from the state vector
%
% Outputs:
% confirmedTracks - a struct array with the tracks confirmed by the tracker.
% egoLane - updated parameters of the ego lane boundaries.
% numTracks - the total number of tracks maintained by the tracker
% mostImportantObject - the track index and ID of the Most Important Object
% (MIO) and the level of Forward Collision Warning (FCW)
%#codegen
% Copyright 2016 The MathWorks, Inc.
%% Tracker Construction and Update
% Define the tracker as a persistent variable to preserve its state between
% calls to this function
persistent tracker
% Create objectDetection inputs to the tracker. Please open the function to
% see memory allocation for code generation.
[detections, egoLane] = processDetections(visionObjects, radarObjects, ...
inertialMeasurementUnit, laneReports, egoLane, time);
% On the first call to the function, tracker is still not defined. The
% following lines of code will define it. Note the use of the first
% detection as a SampleDetection. When generating code, the
% multiObjectTracker requires a SampleDetection to allocate memory for
% detections and tracks on construction.
if isempty(tracker)
tracker = multiObjectTracker(...
'FilterInitializationFcn', @initConstantAccelerationFilter, ...
'AssignmentThreshold', 35, 'ConfirmationParameters', [2 3], ...
'NumCoastingUpdates', 5);
end
% Using the list of objectDetections, return the confirmed tracks, updated
% to time.
confirmedTracks = updateTracks(tracker, detections, time);
numTracks = tracker.NumTracks;
% Find the most important object and calculate the forward collision warning
mostImportantObject = findMostImportantObject(confirmedTracks, egoLane, positionSelector, velocitySelector);
end
%% Defining a Kalman Filter
function filter = initConstantAccelerationFilter(detection)
% This function shows how to configure a constant acceleration filter. The
% input is an objectDetection and the output is a tracking filter.
% For clarity, this function shows how to configure a trackingKF,
% trackingEKF, or trackingUKF for constant acceleration.
%
% Things to consider when creating a filter:
% 1. Defining the motion model and state
% 2. Defining the process noise
% 3. Defining the measurement model
% 4. Mapping the sensor measurements to an initial state vector
% 5. Mapping the sensor measurement noise to a state covariance
% 6. Creating the correct filter
% Step 1: Defining the motion model and state
% In this example, we want to use constant acceleration so:
STF = @constacc; % State-transition function, for EKF and UKF
STFJ = @constaccjac; % State-transition function Jacobian, only for EKF
% The motion model implies that the state is [x;vx;ax;y;vy;ay]
% You could also use constvel and constveljac to setup a constant
% velocity model, constturn and constturnjac to setup a constant turn
% rate model, or write your own models.
% Step 2: Defining the process noise
dt = 0.05; % Known timestep size
sigma = 1; % Magnitude of the unknown acceleration change rate
% The process noise along one dimension
Q1d = [dt^4/4, dt^3/2, dt^2/2; dt^3/2, dt^2, dt; dt^2/2, dt, 1] * sigma^2;
Q = blkdiag(Q1d, Q1d); % 2-D process noise
% Step 3: Defining the measurement model
MF = @fcwmeas; % Measurement function, for EKF and UKF
MJF = @fcwmeasjac; % Measurement Jacobian function, only for EKF
% Step 4: Initializing the state vector based on the measurement.
% The sensors measure [x;vx;y;vy] while the constant acceleration
% model's state is [x;vx;ax;y;vy;ay], so the third and sixth elements
% of the state vector are initialized to zero.
state = [detection.Measurement(1); detection.Measurement(2); 0; detection.Measurement(3); detection.Measurement(4); 0];
% Step 5: Initialize the state covariance based on the measurement
% noise. The parts of the state that are not directly measured are
% assigned a large measurement noise value to account for that.
L = 100; % A large number relative to the measurement noise
stateCov = blkdiag(detection.MeasurementNoise(1:2,1:2), L, detection.MeasurementNoise(3:4,3:4), L);
% Step 6: Creating the correct filter.
% Use 'KF' for trackingKF, 'EKF' for trackingEKF, or 'UKF' for trackingUKF
FilterType = 'EKF';
% Creating the filter:
switch FilterType
case 'EKF'
filter = trackingEKF(STF, MF, state,...
'StateCovariance', stateCov, ...
'MeasurementNoise', detection.MeasurementNoise(1:4,1:4), ...
'StateTransitionJacobianFcn', STFJ, ...
'MeasurementJacobianFcn', MJF, ...
'ProcessNoise', Q ...
);
case 'UKF'
filter = trackingUKF(STF, MF, state, ...
'StateCovariance', stateCov, ...
'MeasurementNoise', detection.MeasurementNoise(1:4,1:4), ...
'Alpha', 1e-1, ...
'ProcessNoise', Q ...
);
case 'KF' % The ConstantAcceleration model is linear and KF can be used
% Define the measurement model: measurement = H * state
% In this case:
% measurement = [x;vx;y;vy] = H * [x;vx;ax;y;vy;ay]
% So, H = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 0 1 0 0; 0 0 0 0 1 0]
%
% Note that ProcessNoise is automatically calculated by the
% ConstantAcceleration motion model
H = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 0 1 0 0; 0 0 0 0 1 0];
filter = trackingKF('MotionModel', '2D Constant Acceleration', ...
'MeasurementModel', H, 'State', state, ...
'MeasurementNoise', detection.MeasurementNoise(1:4,1:4), ...
'StateCovariance', stateCov);
end
end
%% Memory Allocation, Detection Processing and Formatting
% The recorded information has to be processed and formatted before it can
% be used by the tracker. This includes the following steps:
%
% # Allocating memory for the list objectDetection inputs to the tracker.
% Code generation requires that we define exactly the number of objects,
% as well the data types and sizes of each property. We have to use the
% data types and sizes in the recorded data to make sure that code
% generation will be successful.
% # Cleaning the radar detections from unnecessary clutter detections. The
% radar reports many objects that correspond to fixed objects, which
% include: guard-rails, the road median, traffic signs, etc. If these
% detections are used in the tracking, they will create false tracks of
% fixed objects at the edges of the road and therefore should be removed
% prior to calling the tracker. Radar objects are considered non-clutter
% if they are either stationary in front of the car or moving in its
% vicinity.
% # Formatting the detections as input to the tracker, i.e., an array of
% <matlab:doc('objectDetection'), objectDetection> elements. See the
% processVideo and processRadar supporting functions below.
function [detections, egoLane] = processDetections...
(visionFrame, radarFrame, IMUFrame, laneFrame, egoLane, time)
% Inputs are:
% visionFrame - objects reported by the vision sensor for this time frame
% radarFrame - objects reported by the radar sensor for this time frame
% IMUFrame - inertial measurement unit data for this time frame
% laneFrame - lane reports for this time frame
% egoLane - the estimated ego lane
% time - the time corresponding to the time frame
% Remove clutter radar objects
egoLane = processLanes(laneFrame, egoLane);
realRadarObjects = findNonClutterRadarObjects(radarFrame.object,...
radarFrame.numObjects, IMUFrame.velocity, egoLane);
numRadarObjects = numel(realRadarObjects);
% Memory allocation for code generation.
% The recorded detections have several fields that the tracker does not
% use itself, but may be of interest to the user. We store these fields
% in a struct names ObjectAttributes. Since the recorded data uses a
% variety of data types and sizes, we have to allocate the memory for
% code generation using the exact same data types and sizes as the
% recorded data as shown in objAttr.
% We define the set of all the fields we would like to maintain from
% both sensor types. Each sensor will later fill out only the subset
% that pertains to that sensor.
objAttr = struct('VisionID', uint32(0), 'Size', zeros(1,3,'single'), ...
'RadarID', uint16(0), 'Status', uint8(0), 'Amplitude', single(0), ...
'RangeMode', uint8(0));
% Use the object attributes in allocating an objectDetection. This
% objectDetection captures the exact size and types required for
% generating code.
det = {objectDetection(time, zeros(4,1,'single'), 'SensorIndex', 1, ...
'MeasurementNoise', eye(4,'single'), 'MeasurementParameters', {1}, ...
'ObjectClassID', uint8(0), ...
'ObjectAttributes', {objAttr})};
% Finally, allocate the required number of objectDetection objects by
% using the repmat command. It will create a cell array where each cell
% is an objectDetection as defined by det. The allocated memory will
% then be used by processRadar and processVideo that will enter the
% recorded data into the allocated the memory.
detections = repmat(det, [visionFrame.numObjects + numRadarObjects, 1]);
if (visionFrame.numObjects + numRadarObjects) == 0
return;
end
%Process the remaining radar objects
detections = processRadar(detections, realRadarObjects, time, 0);
% Process video objects
detections = processVideo(detections, visionFrame, time, numRadarObjects);
end
%% Processing the Lanes
% Maintans a persistent ego lane estimate
function egoLane = processLanes(laneReports, egoLane)
% Lane boundaries are updated based on the laneReports from the recordings.
% Since some laneReports contain invalid (isValid = false) reports or
% impossible parameter values (-1e9), these lane reports are ignored and
% the previous lane boundary is used.
leftLane = laneReports.left;
rightLane = laneReports.right;
% Check the validity of the reported left lane
cond = (leftLane.isValid && leftLane.confidence) && ...
~(leftLane.headingAngle == -1e9 || leftLane.curvature == -1e9);
if cond
egoLane.left = cast([leftLane.curvature, leftLane.headingAngle, leftLane.offset], 'double');
end
% Check the validity of the reported right lane
cond = (rightLane.isValid && rightLane.confidence) && ...
~(rightLane.headingAngle == -1e9 || rightLane.curvature == -1e9);
if cond
egoLane.right = cast([rightLane.curvature, rightLane.headingAngle, rightLane.offset], 'double');
end
end
%% Removing Clutter from Radar Detections
% Removes radar objects that are considered part of the clutter
function realRadarObjects = findNonClutterRadarObjects(radarObject, numRadarObjects, egoSpeed, egoLane)
% The radar objects include many objects that belong to the clutter.
% Clutter is defined as a stationary object that is not in front of the
% car. The following types of objects pass as non-clutter:
%
% # Any object in front of the car
% # Any moving object in the area of interest around the car, including
% objects that move at a lateral speed around the car
% Allocate memory
normVs = zeros(numRadarObjects, 1);
inLane = zeros(numRadarObjects, 1);
inZone = zeros(numRadarObjects, 1);
% Parameters
LaneWidth = 3.6; % What is considered in front of the car
ZoneWidth = 1.7*LaneWidth; % A wider area of interest
minV = 1; % Any object that moves slower than that is stationary
for j = 1:numRadarObjects
[vx, vy] = calculateGroundSpeed(radarObject(j).velocity(1),radarObject(j).velocity(2),egoSpeed);
normVs(j) = norm([vx,vy]);
laneBoundariesAtObject = [polyval(egoLane.left, radarObject(j).position(1)), polyval(egoLane.right, radarObject(j).position(1))];
laneCenter = mean(laneBoundariesAtObject);
inLane(j) = (abs(radarObject(j).position(2) - laneCenter) <= LaneWidth/2);
inZone(j) = (abs(radarObject(j).position(2) - laneCenter) <= max(abs(vy)*2, ZoneWidth));
end
realRadarObjectsIdx = union(...
intersect(find(normVs > minV), find(inZone == 1)), ...
find(inLane == 1));
realRadarObjects = radarObject(realRadarObjectsIdx);
end
%%%
% *calculateGroundSpeed*
% Calculates the true ground speed of a radar reported object from the
% relative speed and the ego car speed
function [Vx,Vy] = calculateGroundSpeed(Vxi,Vyi,egoSpeed)
% Inputs
% (Vxi,Vyi) : relative object speed
% egoSpeed : ego vehicle speed
% Outputs
% [Vx,Vy] : ground object speed
Vx = Vxi + egoSpeed; % calculate longitudinal ground speed
theta = atan2(Vyi,Vxi); % calculate heading angle
Vy = Vx * tan(theta); % calculate lateral ground speed
end
%% Process Vision Detections
% Converts reported vision objects to a list of |objectDetections|
function detections = processVideo(detections, visionFrame, t, startIndex)
% Process the video objects into objectDetections
numVisionObjects = visionFrame.numObjects;
if numVisionObjects
classToUse = class(visionFrame.object(1).position);
visionMeasCov = cast(diag([2,2,2,100]), classToUse);
% Process Vision Objects:
for i=1:numVisionObjects
object = visionFrame.object(i);
objAttr = struct('VisionID', uint32(object.id), 'Size', object.size, ...
'RadarID', uint16(0), 'Status', uint8(0), 'Amplitude', single(0), ...
'RangeMode', uint8(0));
detections{startIndex+i} = objectDetection(t,...
[object.position(1); object.velocity(1); object.position(2); 0], ...
'SensorIndex', 1, 'MeasurementNoise', visionMeasCov, ...
'MeasurementParameters', {1}, ...
'ObjectClassID', object.classification, ...
'ObjectAttributes', {objAttr});
end
end
end
%% Process Radar Detections
% Converts reported radar objects to a list of |objectDetections|
function detections = processRadar(detections, realRadarObjects, t, startIndex)
% Process the radar objects into objectDetections
numRadarObjects = numel(realRadarObjects);
if numRadarObjects
classToUse = class(realRadarObjects(1).position);
radarMeasCov = cast(diag([2,2,2,100]), classToUse);
% Process Radar Objects:
for i=1:numRadarObjects
object = realRadarObjects(i);
objAttr = struct('VisionID', uint32(0), 'Size', zeros(1,3,'single'), ...
'RadarID', object.id, 'Status', object.status, 'Amplitude', object.amplitude, ...
'RangeMode', object.rangeMode);
detections{startIndex+i} = objectDetection(t, ...
[object.position(1); object.velocity(1); object.position(2); object.velocity(2)], ...
'ObjectClassID', uint8(0), ...
'SensorIndex', 2, 'MeasurementNoise', radarMeasCov, ...
'MeasurementParameters', {2}, ...
'ObjectAttributes', {objAttr});
end
end
end
%%
function measurement = fcwmeas(state, varargin)
% The demo measurements depend on the sensor type, which is reported by the
% objectDetection's MeasurementParameters property. The following two
% sensorID values are used:
% sensorID=1: video objects, the measurement is [x;vx;y].
% sensorID=2: radar objects, the measurement is [x;vx;y;vy].
% The state is:
% Constant velocity state = [x;vx;y;vy]
% Constant turn state = [x;vx;y;vy;omega]
% Constant acceleration state = [x;vx;ax;y;vy;ay]
%#codegen
measurement = zeros(4, 1, 'like', state);
if isempty(varargin)
sensorID = 1;
else
sensorID = varargin{1};
end
if numel(state) < 6 % Constant turn or constant velocity
switch sensorID
case 1 % video
measurement = [state(1:3); 0];
case 2 % radar
measurement = state(1:4);
end
else % Constant acceleration
switch sensorID
case 1 % video
measurement = [state(1:2); state(4); 0];
case 2 % radar
measurement = [state(1:2); state(4:5)];
end
end
end
%%
function jacobian = fcwmeasjac(state, varargin)
% The demo measurements depend on the sensor type, which is reported by the
% objectDetection's MeasurementParameters property. We choose sensorID=1
% for video objects and sensorID=2 for radar objects. The following two
% sensorID values are used:
% sensorID=1: video objects, the measurement is [x;vx;y].
% sensorID=2: radar objects, the measurement is [x;vx;y;vy].
% The state is:
% Constant velocity state = [x;vx;y;vy]
% Constant turn state = [x;vx;y;vy;omega]
% Constant acceleration state = [x;vx;ax;y;vy;ay]
%#codegen
numStates = numel(state);
jacobian = zeros(4, numStates, 'like', state);
if isempty(varargin)
sensorID = 1;
else
sensorID = varargin{1};
end
if numel(state) < 6 % Constant turn or constant velocity
switch sensorID
case 1 % video
jacobian(1,1) = 1;
jacobian(2,2) = 1;
jacobian(3,3) = 1;
case 2 % radar
jacobian(1,1) = 1;
jacobian(2,2) = 1;
jacobian(3,3) = 1;
jacobian(4,4) = 1;
end
else % Constant acceleration
switch sensorID
case 1 % video
jacobian(1,1) = 1;
jacobian(2,2) = 1;
jacobian(3,4) = 1;
case 2 % radar
jacobian(1,1) = 1;
jacobian(2,2) = 1;
jacobian(3,4) = 1;
jacobian(4,5) = 1;
end
end
end
%% findMostImportantObject
% Finding the Most Important Object and Issuing a Forward Collision Warning
% The most important object (MIO) is defined as the track that is in the
% ego lane and is closest in front of the car, i.e., with the smallest
% positive x value. To lower the probability of false alarms, only
% confirmed tracks are considered.
%
% Once the MIO is found, the relative speed between the car and MIO is
% calculated. The relative distance and relative speed determine the
% forward collision warning. There are 3 cases of FCW:
%
% # Safe: There is no car in the ego lane (no MIO), the MIO is moving away
% from the car, or the distance is maintained constant. This case is
% indicated by a green color.
% # Caution: The MIO is moving closer to the car, but is still at a
% distance above the FCW distance. FCW distance is calculated using the
% Euro NCAP AEB Test Protocol. Note that this distance varies with the
% relative speed between the MIO and the car, and is greater when the
% closing speed is higher. This case is indicated by a yellow color.
% # Warn: The MIO is moving closer to the car, and its distance is less
% than the FCW distance. This case is indicated by a red color.
%
% Euro NCAP AEB Test Protocol defines the following distance calculation:
%%
% $d_{FCW} = 1.2 * v_{rel} + \frac{v_{rel}^2}{2a_{max}}$
%
% Where:
%
% $d_{FCW}$ is the forward collision warning distance
%
% $v_{rel}$ is the relative velocity between the two vehicles
%
% $a_{max}$ is the maximum deceleration defined to be 40% of the gravity acceleration
function mostImportantObject = findMostImportantObject(confirmedTracks,egoLane,positionSelector,velocitySelector)
% Initialize outputs and parameters
MIO = zeros(0, 'uint32'); % By default, there is no MIO
trackIndex = []; % By default, there is no MIO
FCW = 3; % By default, the FCW is 'safe'
threatColor = 'green'; % By default, the threat color is green
maxX = single(1000); % Beyond sensor range. No track can exceed it
gAccel = 9.8; % Constant gravity acceleration, in m/s^2
maxDeceleration = 0.4 * gAccel; % EuroNCAP AEB definition
delayTime = 1.2; % Delay time for a driver before starting to break, in seconds
for i = 1:numel(confirmedTracks)
position = positionSelector * confirmedTracks(i).State;
x = position(1);
y = position(2);
velocity = velocitySelector * confirmedTracks(i).State;
relSpeed = velocity(1); % The relative speed between the cars, along the lane
if x < maxX && x > 0 % No point checking otherwise
yleftLane = polyval(egoLane.left, x);
yrightLane = polyval(egoLane.right, x);
if (yrightLane <= y) && (y <= yleftLane)
maxX = x;
trackIndex = i;
MIO = confirmedTracks(i).TrackID;
if relSpeed < 0 % Relative speed indicates object is getting closer
% Calculate expected braking distance according to
% Euro NCAP AEB Test Protocol
d = abs(relSpeed) * delayTime + relSpeed^2 / 2 / maxDeceleration;
if x <= d % 'warn'
FCW = 1;
threatColor = 'red';
else % 'caution'
FCW = 2;
threatColor = 'yellow';
end
end
end
end
end
mostImportantObject = struct('ObjectID', MIO, 'TrackIndex', trackIndex, 'Warning', FCW, 'ThreatColor', threatColor);
end