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openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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openpilot_comma/selfdrive/modeld/models/driving.cc

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#include "selfdrive/modeld/models/driving.h"
#include <fcntl.h>
#include <unistd.h>
#include <cassert>
#include <cstring>
#include <eigen3/Eigen/Dense>
#include "selfdrive/common/clutil.h"
#include "selfdrive/common/params.h"
#include "selfdrive/common/timing.h"
constexpr float FCW_THRESHOLD_5MS2_HIGH = 0.15;
constexpr float FCW_THRESHOLD_5MS2_LOW = 0.05;
constexpr float FCW_THRESHOLD_3MS2 = 0.7;
float prev_brake_5ms2_probs[5] = {0,0,0,0,0};
float prev_brake_3ms2_probs[3] = {0,0,0};
// #define DUMP_YUV
template<class T, size_t size>
constexpr const kj::ArrayPtr<const T> to_kj_array_ptr(const std::array<T, size> &arr) {
return kj::ArrayPtr(arr.data(), arr.size());
}
void model_init(ModelState* s, cl_device_id device_id, cl_context context) {
s->frame = new ModelFrame(device_id, context);
#ifdef USE_THNEED
s->m = std::make_unique<ThneedModel>("../../models/supercombo.thneed", &s->output[0], NET_OUTPUT_SIZE, USE_GPU_RUNTIME);
#elif USE_ONNX_MODEL
s->m = std::make_unique<ONNXModel>("../../models/supercombo.onnx", &s->output[0], NET_OUTPUT_SIZE, USE_GPU_RUNTIME);
#else
s->m = std::make_unique<SNPEModel>("../../models/supercombo.dlc", &s->output[0], NET_OUTPUT_SIZE, USE_GPU_RUNTIME);
#endif
#ifdef TEMPORAL
s->m->addRecurrent(&s->output[OUTPUT_SIZE], TEMPORAL_SIZE);
#endif
#ifdef DESIRE
s->m->addDesire(s->pulse_desire, DESIRE_LEN);
#endif
#ifdef TRAFFIC_CONVENTION
const int idx = Params().getBool("IsRHD") ? 1 : 0;
s->traffic_convention[idx] = 1.0;
s->m->addTrafficConvention(s->traffic_convention, TRAFFIC_CONVENTION_LEN);
#endif
}
ModelDataRaw model_eval_frame(ModelState* s, cl_mem yuv_cl, int width, int height,
const mat3 &transform, float *desire_in) {
#ifdef DESIRE
if (desire_in != NULL) {
for (int i = 1; i < DESIRE_LEN; i++) {
// Model decides when action is completed
// so desire input is just a pulse triggered on rising edge
if (desire_in[i] - s->prev_desire[i] > .99) {
s->pulse_desire[i] = desire_in[i];
} else {
s->pulse_desire[i] = 0.0;
}
s->prev_desire[i] = desire_in[i];
}
}
#endif
//for (int i = 0; i < NET_OUTPUT_SIZE; i++) { printf("%f ", s->output[i]); } printf("\n");
// if getInputBuf is not NULL, net_input_buf will be
auto net_input_buf = s->frame->prepare(yuv_cl, width, height, transform, static_cast<cl_mem*>(s->m->getInputBuf()));
s->m->execute(net_input_buf, s->frame->buf_size);
// net outputs
ModelDataRaw net_outputs {
.plans = (ModelDataRawPlans*)&s->output[PLAN_IDX],
.lane_lines = (ModelDataRawLaneLines*)&s->output[LL_IDX],
.road_edges = (ModelDataRawRoadEdges*)&s->output[RE_IDX],
.leads = (ModelDataRawLeads*)&s->output[LEAD_IDX],
.meta = &s->output[DESIRE_STATE_IDX],
.pose = (ModelDataRawPose*)&s->output[POSE_IDX],
};
return net_outputs;
}
void model_free(ModelState* s) {
delete s->frame;
}
void fill_sigmoid(const float *input, float *output, int len, int stride) {
for (int i=0; i<len; i++) {
output[i] = sigmoid(input[i*stride]);
}
}
void fill_lead(cereal::ModelDataV2::LeadDataV3::Builder lead, const ModelDataRawLeads &leads, int t_idx, float prob_t) {
std::array<float, LEAD_TRAJ_LEN> lead_t = {0.0, 2.0, 4.0, 6.0, 8.0, 10.0};
const auto &best_prediction = leads.get_best_prediction(t_idx);
lead.setProb(sigmoid(leads.prob[t_idx]));
lead.setProbTime(prob_t);
std::array<float, LEAD_TRAJ_LEN> lead_x, lead_y, lead_v, lead_a;
std::array<float, LEAD_TRAJ_LEN> lead_x_std, lead_y_std, lead_v_std, lead_a_std;
for (int i=0; i<LEAD_TRAJ_LEN; i++) {
lead_x[i] = best_prediction.mean[i].x;
lead_y[i] = best_prediction.mean[i].y;
lead_v[i] = best_prediction.mean[i].velocity;
lead_a[i] = best_prediction.mean[i].acceleration;
lead_x_std[i] = exp(best_prediction.std[i].x);
lead_y_std[i] = exp(best_prediction.std[i].y);
lead_v_std[i] = exp(best_prediction.std[i].velocity);
lead_a_std[i] = exp(best_prediction.std[i].acceleration);
}
lead.setT(to_kj_array_ptr(lead_t));
lead.setX(to_kj_array_ptr(lead_x));
lead.setY(to_kj_array_ptr(lead_y));
lead.setV(to_kj_array_ptr(lead_v));
lead.setA(to_kj_array_ptr(lead_a));
lead.setXStd(to_kj_array_ptr(lead_x_std));
lead.setYStd(to_kj_array_ptr(lead_y_std));
lead.setVStd(to_kj_array_ptr(lead_v_std));
lead.setAStd(to_kj_array_ptr(lead_a_std));
}
void fill_meta(cereal::ModelDataV2::MetaData::Builder meta, const float *meta_data) {
float desire_state_softmax[DESIRE_LEN];
float desire_pred_softmax[4*DESIRE_LEN];
softmax(&meta_data[0], desire_state_softmax, DESIRE_LEN);
for (int i=0; i<4; i++) {
softmax(&meta_data[DESIRE_LEN + OTHER_META_SIZE + i*DESIRE_LEN],
&desire_pred_softmax[i*DESIRE_LEN], DESIRE_LEN);
}
float gas_disengage_sigmoid[NUM_META_INTERVALS];
float brake_disengage_sigmoid[NUM_META_INTERVALS];
float steer_override_sigmoid[NUM_META_INTERVALS];
float brake_3ms2_sigmoid[NUM_META_INTERVALS];
float brake_4ms2_sigmoid[NUM_META_INTERVALS];
float brake_5ms2_sigmoid[NUM_META_INTERVALS];
fill_sigmoid(&meta_data[DESIRE_LEN+1], gas_disengage_sigmoid, NUM_META_INTERVALS, META_STRIDE);
fill_sigmoid(&meta_data[DESIRE_LEN+2], brake_disengage_sigmoid, NUM_META_INTERVALS, META_STRIDE);
fill_sigmoid(&meta_data[DESIRE_LEN+3], steer_override_sigmoid, NUM_META_INTERVALS, META_STRIDE);
fill_sigmoid(&meta_data[DESIRE_LEN+4], brake_3ms2_sigmoid, NUM_META_INTERVALS, META_STRIDE);
fill_sigmoid(&meta_data[DESIRE_LEN+5], brake_4ms2_sigmoid, NUM_META_INTERVALS, META_STRIDE);
fill_sigmoid(&meta_data[DESIRE_LEN+6], brake_5ms2_sigmoid, NUM_META_INTERVALS, META_STRIDE);
//fill_sigmoid(&meta_data[DESIRE_LEN+7], GAS PRESSED, NUM_META_INTERVALS, META_STRIDE);
std::memmove(prev_brake_5ms2_probs, &prev_brake_5ms2_probs[1], 4*sizeof(float));
std::memmove(prev_brake_3ms2_probs, &prev_brake_3ms2_probs[1], 2*sizeof(float));
prev_brake_5ms2_probs[4] = brake_5ms2_sigmoid[0];
prev_brake_3ms2_probs[2] = brake_3ms2_sigmoid[0];
bool above_fcw_threshold = true;
for (int i=0; i<5; i++) {
float threshold = i < 2 ? FCW_THRESHOLD_5MS2_LOW : FCW_THRESHOLD_5MS2_HIGH;
above_fcw_threshold = above_fcw_threshold && prev_brake_5ms2_probs[i] > threshold;
}
for (int i=0; i<3; i++) {
above_fcw_threshold = above_fcw_threshold && prev_brake_3ms2_probs[i] > FCW_THRESHOLD_3MS2;
}
auto disengage = meta.initDisengagePredictions();
disengage.setT({2,4,6,8,10});
disengage.setGasDisengageProbs(gas_disengage_sigmoid);
disengage.setBrakeDisengageProbs(brake_disengage_sigmoid);
disengage.setSteerOverrideProbs(steer_override_sigmoid);
disengage.setBrake3MetersPerSecondSquaredProbs(brake_3ms2_sigmoid);
disengage.setBrake4MetersPerSecondSquaredProbs(brake_4ms2_sigmoid);
disengage.setBrake5MetersPerSecondSquaredProbs(brake_5ms2_sigmoid);
meta.setEngagedProb(sigmoid(meta_data[DESIRE_LEN]));
meta.setDesirePrediction(desire_pred_softmax);
meta.setDesireState(desire_state_softmax);
meta.setHardBrakePredicted(above_fcw_threshold);
}
template<size_t size>
void fill_xyzt(cereal::ModelDataV2::XYZTData::Builder xyzt, const std::array<float, size> &t,
const std::array<float, size> &x, const std::array<float, size> &y, const std::array<float, size> &z) {
xyzt.setT(to_kj_array_ptr(t));
xyzt.setX(to_kj_array_ptr(x));
xyzt.setY(to_kj_array_ptr(y));
xyzt.setZ(to_kj_array_ptr(z));
}
template<size_t size>
void fill_xyzt(cereal::ModelDataV2::XYZTData::Builder xyzt, const std::array<float, size> &t,
const std::array<float, size> &x, const std::array<float, size> &y, const std::array<float, size> &z,
const std::array<float, size> &x_std, const std::array<float, size> &y_std, const std::array<float, size> &z_std) {
fill_xyzt(xyzt, t, x, y, z);
xyzt.setXStd(to_kj_array_ptr(x_std));
xyzt.setYStd(to_kj_array_ptr(y_std));
xyzt.setZStd(to_kj_array_ptr(z_std));
}
void fill_plan(cereal::ModelDataV2::Builder &framed, const ModelDataRawPlanPrediction &plan) {
std::array<float, TRAJECTORY_SIZE> pos_x, pos_y, pos_z;
std::array<float, TRAJECTORY_SIZE> pos_x_std, pos_y_std, pos_z_std;
std::array<float, TRAJECTORY_SIZE> vel_x, vel_y, vel_z;
std::array<float, TRAJECTORY_SIZE> rot_x, rot_y, rot_z;
std::array<float, TRAJECTORY_SIZE> rot_rate_x, rot_rate_y, rot_rate_z;
for(int i=0; i<TRAJECTORY_SIZE; i++) {
pos_x[i] = plan.mean[i].position.x;
pos_y[i] = plan.mean[i].position.y;
pos_z[i] = plan.mean[i].position.z;
pos_x_std[i] = exp(plan.std[i].position.x);
pos_y_std[i] = exp(plan.std[i].position.y);
pos_z_std[i] = exp(plan.std[i].position.z);
vel_x[i] = plan.mean[i].velocity.x;
vel_y[i] = plan.mean[i].velocity.y;
vel_z[i] = plan.mean[i].velocity.z;
rot_x[i] = plan.mean[i].rotation.x;
rot_y[i] = plan.mean[i].rotation.y;
rot_z[i] = plan.mean[i].rotation.z;
rot_rate_x[i] = plan.mean[i].rotation_rate.x;
rot_rate_y[i] = plan.mean[i].rotation_rate.y;
rot_rate_z[i] = plan.mean[i].rotation_rate.z;
}
fill_xyzt(framed.initPosition(), T_IDXS_FLOAT, pos_x, pos_y, pos_z, pos_x_std, pos_y_std, pos_z_std);
fill_xyzt(framed.initVelocity(), T_IDXS_FLOAT, vel_x, vel_y, vel_z);
fill_xyzt(framed.initOrientation(), T_IDXS_FLOAT, rot_x, rot_y, rot_z);
fill_xyzt(framed.initOrientationRate(), T_IDXS_FLOAT, rot_rate_x, rot_rate_y, rot_rate_z);
}
void fill_lane_lines(cereal::ModelDataV2::Builder &framed, const std::array<float, TRAJECTORY_SIZE> &plan_t,
const ModelDataRawLaneLines &lanes) {
std::array<float, TRAJECTORY_SIZE> left_far_y, left_far_z;
std::array<float, TRAJECTORY_SIZE> left_near_y, left_near_z;
std::array<float, TRAJECTORY_SIZE> right_near_y, right_near_z;
std::array<float, TRAJECTORY_SIZE> right_far_y, right_far_z;
for (int j=0; j<TRAJECTORY_SIZE; j++) {
left_far_y[j] = lanes.mean.left_far[j].y;
left_far_z[j] = lanes.mean.left_far[j].z;
left_near_y[j] = lanes.mean.left_near[j].y;
left_near_z[j] = lanes.mean.left_near[j].z;
right_near_y[j] = lanes.mean.right_near[j].y;
right_near_z[j] = lanes.mean.right_near[j].z;
right_far_y[j] = lanes.mean.right_far[j].y;
right_far_z[j] = lanes.mean.right_far[j].z;
}
auto lane_lines = framed.initLaneLines(4);
fill_xyzt(lane_lines[0], plan_t, X_IDXS_FLOAT, left_far_y, left_far_z);
fill_xyzt(lane_lines[1], plan_t, X_IDXS_FLOAT, left_near_y, left_near_z);
fill_xyzt(lane_lines[2], plan_t, X_IDXS_FLOAT, right_near_y, right_near_z);
fill_xyzt(lane_lines[3], plan_t, X_IDXS_FLOAT, right_far_y, right_far_z);
framed.setLaneLineStds({
exp(lanes.std.left_far[0].y),
exp(lanes.std.left_near[0].y),
exp(lanes.std.right_near[0].y),
exp(lanes.std.right_far[0].y),
});
framed.setLaneLineProbs({
sigmoid(lanes.prob.left_far.val),
sigmoid(lanes.prob.left_near.val),
sigmoid(lanes.prob.right_near.val),
sigmoid(lanes.prob.right_far.val),
});
}
void fill_road_edges(cereal::ModelDataV2::Builder &framed, const std::array<float, TRAJECTORY_SIZE> &plan_t,
const ModelDataRawRoadEdges &edges) {
std::array<float, TRAJECTORY_SIZE> left_y, left_z;
std::array<float, TRAJECTORY_SIZE> right_y, right_z;
for (int j=0; j<TRAJECTORY_SIZE; j++) {
left_y[j] = edges.mean.left[j].y;
left_z[j] = edges.mean.left[j].z;
right_y[j] = edges.mean.right[j].y;
right_z[j] = edges.mean.right[j].z;
}
auto road_edges = framed.initRoadEdges(2);
fill_xyzt(road_edges[0], plan_t, X_IDXS_FLOAT, left_y, left_z);
fill_xyzt(road_edges[1], plan_t, X_IDXS_FLOAT, right_y, right_z);
framed.setRoadEdgeStds({
exp(edges.std.left[0].y),
exp(edges.std.right[0].y),
});
}
void fill_model(cereal::ModelDataV2::Builder &framed, const ModelDataRaw &net_outputs) {
const auto &best_plan = net_outputs.plans->get_best_prediction();
std::array<float, TRAJECTORY_SIZE> plan_t;
std::fill_n(plan_t.data(), plan_t.size(), NAN);
plan_t[0] = 0.0;
for (int xidx=1, tidx=0; xidx<TRAJECTORY_SIZE; xidx++) {
// increment tidx until we find an element that's further away than the current xidx
for (int next_tid = tidx + 1; next_tid < TRAJECTORY_SIZE && best_plan.mean[next_tid].position.x < X_IDXS[xidx]; next_tid++) {
tidx++;
}
if (tidx == TRAJECTORY_SIZE - 1) {
// if the Plan doesn't extend far enough, set plan_t to the max value (10s), then break
plan_t[xidx] = T_IDXS[TRAJECTORY_SIZE - 1];
break;
}
// interpolate to find `t` for the current xidx
float current_x_val = best_plan.mean[tidx].position.x;
float next_x_val = best_plan.mean[tidx+1].position.x;
float p = (X_IDXS[xidx] - current_x_val) / (next_x_val - current_x_val);
plan_t[xidx] = p * T_IDXS[tidx+1] + (1 - p) * T_IDXS[tidx];
}
fill_plan(framed, best_plan);
fill_lane_lines(framed, plan_t, *net_outputs.lane_lines);
fill_road_edges(framed, plan_t, *net_outputs.road_edges);
// meta
fill_meta(framed.initMeta(), net_outputs.meta);
// leads
auto leads = framed.initLeadsV3(LEAD_MHP_SELECTION);
std::array<float, LEAD_MHP_SELECTION> t_offsets = {0.0, 2.0, 4.0};
for (int i=0; i<LEAD_MHP_SELECTION; i++) {
fill_lead(leads[i], *net_outputs.leads, i, t_offsets[i]);
}
}
void model_publish(PubMaster &pm, uint32_t vipc_frame_id, uint32_t frame_id, float frame_drop,
const ModelDataRaw &net_outputs, uint64_t timestamp_eof,
float model_execution_time, kj::ArrayPtr<const float> raw_pred) {
const uint32_t frame_age = (frame_id > vipc_frame_id) ? (frame_id - vipc_frame_id) : 0;
MessageBuilder msg;
auto framed = msg.initEvent().initModelV2();
framed.setFrameId(vipc_frame_id);
framed.setFrameAge(frame_age);
framed.setFrameDropPerc(frame_drop * 100);
framed.setTimestampEof(timestamp_eof);
framed.setModelExecutionTime(model_execution_time);
if (send_raw_pred) {
framed.setRawPredictions(raw_pred.asBytes());
}
fill_model(framed, net_outputs);
pm.send("modelV2", msg);
}
void posenet_publish(PubMaster &pm, uint32_t vipc_frame_id, uint32_t vipc_dropped_frames,
const ModelDataRaw &net_outputs, uint64_t timestamp_eof) {
MessageBuilder msg;
auto v_mean = net_outputs.pose->velocity_mean;
auto r_mean = net_outputs.pose->rotation_mean;
auto v_std = net_outputs.pose->velocity_std;
auto r_std = net_outputs.pose->rotation_std;
auto posenetd = msg.initEvent(vipc_dropped_frames < 1).initCameraOdometry();
posenetd.setTrans({v_mean.x, v_mean.y, v_mean.z});
posenetd.setRot({r_mean.x, r_mean.y, r_mean.z});
posenetd.setTransStd({exp(v_std.x), exp(v_std.y), exp(v_std.z)});
posenetd.setRotStd({exp(r_std.x), exp(r_std.y), exp(r_std.z)});
posenetd.setTimestampEof(timestamp_eof);
posenetd.setFrameId(vipc_frame_id);
pm.send("cameraOdometry", msg);
}