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Merge branch 'smail' into lbaumberger

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lbaumberger
2026-04-27 14:25:29 +02:00
parent 1b79bd15f4 cde82faa4d
commit 1cf29bfdd8
5 changed files with 174 additions and 227 deletions
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board.hpp
+3 -46
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@@ -1,52 +1,9 @@
#pragma once #pragma once
#include "vector2d.hpp" #include "vector2d.hpp"
struct Obstacle {
Vector2d start;
Vector2d end;
Obstacle() : start(0, 0), end(0, 0) {}
Obstacle(Vector2d s, Vector2d e) : start(s), end(e) {}
// Find the point of the obstacle that is nearest to p
// # 15 flops
Vector2d find_nearest_obstacle_point(Vector2d p) {
// # 2 ADDs
Vector2d obstacle_v = end - start;
// # 9 flops = 2 ADDs, 2 MULTs, 1 ADD, 1 DIV, 2 MULTs, 1 ADD
double projection = (p - start).dot(obstacle_v) / obstacle_v.dot(obstacle_v);
if (projection < 0.0)
projection = 0.0;
else if (projection > 1.0)
projection = 1.0;
// # 4 flops = 2 ADDs, 2 MULTs
return start + obstacle_v * projection;
}
};
struct Board { struct Board {
double length, width; double length, width;
Obstacle *obstacles; // array of obstacles (represented as lines) that are present on the board Vector2d *borders; // array containing the position of each obstacle
int n_obstacles; // number of obstacles int n_borders; // number of obstacles
Board(double l, double w, int n, Vector2d *border) : length(l), width(w), n_borders(n), borders(border) {}
Board(double l, double w, int n, Obstacle *o) : length(l), width(w), n_obstacles(n + 4) {
obstacles = new Obstacle[n + 4];
for (int i = 0; i < n; i++) {
obstacles[i] = o[i];
}
// borders of the board: bottom, right, top, left
obstacles[n] = Obstacle(Vector2d(0, 0), Vector2d(l, 0));
obstacles[n + 1] = Obstacle(Vector2d(l, 0), Vector2d(l, w));
obstacles[n + 2] = Obstacle(Vector2d(l, w), Vector2d(0, w));
obstacles[n + 3] = Obstacle(Vector2d(0, w), Vector2d(0, 0));
}
~Board() { delete[] obstacles; }
}; };
forces.cpp
+69 -49
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@@ -1,32 +1,35 @@
#include "forces.hpp" #include "forces.hpp"
#include <math.h> #include <math.h>
// # 8 flops = 5 MULTs, 2 ADD, 1 SQRT // # Done
double w(Vector2d e, Vector2d f) { double w(Vector2d e, Vector2d f) {
if (e.dot(f) >= f.norm() * cos_phi) if (e.dot(f) >= f.norm() * cos_phi)
return 1.0; return 1.0;
else
return c; return c;
} }
// # 2 DIVs // # Done
double g(double v_max_alpha, double w_alpha_norm) { //
if (w_alpha_norm <= v_max_alpha) { // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
return 1; //
}
return v_max_alpha / w_alpha_norm;
}
// # 8 flops
void compute_acceleration_term(Pedestrian *p) { void compute_acceleration_term(Pedestrian *p) {
// # 2 flops
// $v_{\alpha}^{0}\vec{e}_{\alpha}$
Vector2d desired_velocity = p->e * p->desired_v0; Vector2d desired_velocity = p->e * p->desired_v0;
// $\vec{v}_{\alpha}(t)$
Vector2d actual_velocity = p->curr_vel; Vector2d actual_velocity = p->curr_vel;
// # 6 flops = 4 ADDs, 2 DIVs
p->f_i += (desired_velocity - actual_velocity) / tau_alpha; double tau_alpha_inv = 1 / tau_alpha;
p->F_i += (desired_velocity - actual_velocity) * tau_alpha_inv;
} }
// # 55(N - 1) = 55N - 55 flops // # Done
//
// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
//
void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestrian *p, int N) { void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestrian *p, int N) {
for (int i = 0; i < N; i++) { for (int i = 0; i < N; i++) {
@@ -40,61 +43,78 @@ void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestr
// continue; // continue;
// } // }
// # 2 ADDs // $\vec{r}_{\alpha\beta}$
Vector2d r_ab = p->curr_pos - pedestrians[i].curr_pos; Vector2d r_ab = p->curr_pos - pedestrians[i].curr_pos;
// # 4 flops = 1 SQRT, 2 MULTs, 1 ADD // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
double r_ab_norm = r_ab.norm(); double r_ab_norm = r_ab.norm();
Vector2d e_b = pedestrians[i].e; // $\vec{e}_{\beta}$
Vector2d e_beta = pedestrians[i].e;
// # 2 MULTs // $v_\beta \Delta t$
double s_b = pedestrians[i].curr_vel.norm() * delta_t; double v_beta_delta_t = pedestrians[i].desired_v0 * delta_t;
// # 4 flops = 2 ADDs, 2 MULTs // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
Vector2d r_ab2 = r_ab - (e_b * s_b); Vector2d r_ab_minus_v_beta_delta_t_e_beta = r_ab - (e_beta * v_beta_delta_t);
// # 4 flops = 1 SQRT, 2 MULTs, 1 ADD // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
double r_ab2_norm = r_ab2.norm(); double r_ab_minus_v_beta_delta_t_e_beta_norm = r_ab_minus_v_beta_delta_t_e_beta.norm();
// # 7 flops = 1 SQRT, 3 ADDs, 2 MULTs, 1 DIV // (4):
double b = sqrt((r_ab_norm + r_ab2_norm) * (r_ab_norm + r_ab2_norm) - s_b * s_b) / 2; //
// $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
//
// # 12 flops = 6 DIVs, 3 ADDs, 3 MULTs double b = sqrt((r_ab_norm + r_ab_minus_v_beta_delta_t_e_beta_norm) * (r_ab_norm + r_ab_minus_v_beta_delta_t_e_beta_norm) - (v_beta_delta_t) * (v_beta_delta_t)) / 2;
Vector2d b_grad = (r_ab / r_ab_norm + r_ab2 / r_ab2_norm) * (r_ab_norm + r_ab2_norm) / (4 * b);
// # 6 flops = 3 MULTs, 2 DIVs, 1 EXP // (7):
Vector2d f_ped = b_grad * ((V0_AB / SIGMA) * (exp(-b / SIGMA))); //
// $\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert) (\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{4b}$
//
//
Vector2d delta_r_alpha_beta_b = (((r_ab / r_ab_norm) + (r_ab_minus_v_beta_delta_t_e_beta / r_ab_minus_v_beta_delta_t_e_beta_norm)) * (r_ab_norm + r_ab_minus_v_beta_delta_t_e_beta_norm)) / (4 * b);
// # 14 flops: 9 MULTs, 4 ADDs, 1 SQRT // (5):
p->f_i += f_ped * w(p->e, f_ped * -1); //
// $\vec{f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vec{r}_{\alpha \beta}}b$
//
//
Vector2d f_alpha_beta_r_alpha_beta = delta_r_alpha_beta_b * ((V0_AB / SIGMA) * (exp(-b / SIGMA)));
// (11):
//
// $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta}) = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta})\vec{f}_{\alpha\beta}(\vec{r}_{\alpha} - \vec{r}_{\beta})$
//
// Add repulsive effects from other pedestrians to current pedestrian's social force
p->F_i += f_alpha_beta_r_alpha_beta * w(p->e, f_alpha_beta_r_alpha_beta * -1);
} }
} }
// # Assume that the four borders are also 4 obstacles // # Done
// # 48 n_obstacles flops = 192 flops void compute_repulsive_effects_from_borders(Board &B, Pedestrian *p) {
void compute_repulsive_effects_from_borders(Board &board, Pedestrian *p) {
Obstacle *obstacles = board.obstacles; Vector2d *borders = B.borders;
int n_borders = board.n_obstacles; int n_borders = B.n_borders;
for (int i = 0; i < n_borders; i++) { for (int i = 0; i < n_borders; i++) {
Obstacle obstacle = obstacles[i]; Vector2d border = borders[i];
// # 17 flops // $\vec{r}_{\alpha B}$
Vector2d r_aB = p->curr_pos - obstacle.find_nearest_obstacle_point(p->curr_pos); Vector2d r_alpha_B = p->curr_pos - border;
// # 17 flops = 2 DIVs, 1 SQRT, 2 MULTs, 1 ADD, 2 MULTs, 1 DIV, 2 MULTs, 1 EXP, 1 SQRT, 2 MULTs, 1 ADD, 1 DIV //
Vector2d f_obst = (r_aB / r_aB.norm()) * (U0_AB / R) * std::exp(-r_aB.norm() / R); // $\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
//
// # 2 ADDs Vector2d F_alpha_B = (r_alpha_B / r_alpha_B.norm()) * (U0_AB / R) * std::exp(-r_alpha_B.norm() / R);
// p->f_i += f_obst; //commented since this version dont takes the field of view / angle to the border into account
// I have read the paper again and on page 6 they say // (12):
// However, the formulas above for attractive and repulsive effects only hold for situations that are perceived in the desired direction e_alpha(t) of motion. //
// I assumed that with formulas above its also meant for equation 8 (even though not explicitly mentioned their) // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{ B}) = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B})\vec{F}_{\alpha B}(\vec{r}_{\alpha} - \vec{r}_{ B})$
// # 14 flops: 9 MULTs, 4 ADDS, 1 SQRT //
p->f_i += f_obst * w(p->e, f_obst * -1);
p->F_i += F_alpha_B * w(p->e, F_alpha_B * (-1));
} }
} }
forces.hpp
+18 -5
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@@ -3,33 +3,46 @@
#include "pedestrian.hpp" #include "pedestrian.hpp"
#include <cmath> #include <cmath>
// $\tau_{\alpha}$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double tau_alpha = 0.5; const double tau_alpha = 0.5;
// $V^0_{\alpha \beta}$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double V0_AB = 2.1; const double V0_AB = 2.1;
// $\sigma$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double SIGMA = 0.3; const double SIGMA = 0.3;
// $U^0_{\alpha B}$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double U0_AB = 10.0; const double U0_AB = 10.0;
// $R$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double R = 0.2; const double R = 0.2;
// $c$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double c = 0.5; const double c = 0.5;
const double phi = 100.0 / 180.0 * M_PI; // $\varphi$
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double phi = 100.0;
const double cos_phi = cos(phi); const double cos_phi = cos(phi);
// $\varDelta_{t}$
// step size = 0.2 seconds
// https://arxiv.org/pdf/cond-mat/9805244, page 8
const double delta_t = 2; const double delta_t = 2;
const double dt = 0.01;
// # Optional (forces.cpp: compute_repulsive_effects_from_pedestrians) // # Optional (forces.cpp: compute_repulsive_effects_from_pedestrians)
// const double DISTANCE = 2.0; // const double DISTANCE = 2.0;
double w(Vector2d e, Vector2d f); double w(Vector2d e, Vector2d f);
double g(double v_max_alpha, double w_alpha_norm);
void compute_acceleration_term(Pedestrian *p); void compute_acceleration_term(Pedestrian *p);
void compute_repulsive_effects_from_pedestrians(Pedestrian *P, Pedestrian *p, int N); void compute_repulsive_effects_from_pedestrians(Pedestrian *P, Pedestrian *p, int N);
main.cpp
+43 -115
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@@ -2,15 +2,14 @@
#include "forces.hpp" #include "forces.hpp"
#include "math.h" #include "math.h"
#include "pedestrian.hpp" #include "pedestrian.hpp"
#include "tsc_x86.h"
#include "vector2d.hpp" #include "vector2d.hpp"
#include <fstream>
#include <iostream>
#include <tuple>
#include <vector>
#define BOARD_WIDTH 10.0 double g(double v_max_alpha, double w_alpha_norm) {
#define BOARD_LENGTH 50.0 if (w_alpha_norm <= v_max_alpha) {
return 1;
}
return v_max_alpha / w_alpha_norm;
}
using namespace std; using namespace std;
@@ -24,140 +23,69 @@ using namespace std;
Pedestrian *init_pedestrians(int n_p) { Pedestrian *init_pedestrians(int n_p) {
Pedestrian *P = new Pedestrian[n_p]; Pedestrian *P = new Pedestrian[n_p];
std::random_device rd; // use for truly random simulation for (int i = 0; i < n_p; i++) {
// std::mt19937 gen(rd()); // use rd instead of 42 as seed for real randomness P[i] = Pedestrian();
std::mt19937 gen(42); // use fixed seed for reproducibility
std::normal_distribution<double> velocity_gen(1.34, 0.26); // 1.34 and 0.26 are the mean and stdrd deviation used in paper
std::uniform_real_distribution<double> x_gen(2, BOARD_LENGTH - 2);
std::uniform_real_distribution<double> y_gen(2, BOARD_WIDTH - 2);
for (int i = 0; i < n_p / 2; i++) {
double y = y_gen(gen);
Vector2d start_pos = Vector2d(x_gen(gen), y);
Vector2d destination = Vector2d(BOARD_LENGTH + 300, BOARD_WIDTH / 2);
// double desired_v0 = velocity_gen(gen);
double desired_v0;
do {
desired_v0 = velocity_gen(gen);
} while (desired_v0 <= 0.1); // to avoid small values (Way below the social force -> that would make the walk backwards) (we discovered some weird stuff in the simulation)
double v_max = 1.3 * desired_v0;
P[i] = Pedestrian(WHITE, (destination - start_pos) / (destination - start_pos).norm() * desired_v0, start_pos, (destination - start_pos) / (destination - start_pos).norm(), v_max, desired_v0, destination, Vector2d());
}
for (int i = n_p / 2; i < n_p; i++) {
double y = y_gen(gen);
Vector2d start_pos = Vector2d(x_gen(gen), y);
Vector2d destination = Vector2d(-300, BOARD_WIDTH / 2);
// double desired_v0 = velocity_gen(gen);
double desired_v0;
do {
desired_v0 = velocity_gen(gen);
} while (desired_v0 <= 0.1);
double v_max = 1.3 * desired_v0;
P[i] = Pedestrian(BLACK, (destination - start_pos) / (destination - start_pos).norm() * desired_v0, start_pos, (destination - start_pos) / (destination - start_pos).norm(), v_max, desired_v0, destination, Vector2d(0, 0));
} }
return P; return P;
} }
Board init_board(int width, int length) {
Vector2d obs[] = {Vector2d(2.0, 1.0), Vector2d(4.0, 1.0), Vector2d(6.0, 2.0), Vector2d(6.0, 4.0)};
return Board(width, length, 4, obs);
}
int main(int argc, const char *argv[]) { int main(int argc, const char *argv[]) {
Obstacle obs[] = {}; Board B = init_board(10, 15);
Board board = Board(BOARD_LENGTH, BOARD_WIDTH, 0, obs);
int n_p = atoi(argv[1]); int n_p = atoi(argv[1]);
int n_iter = atoi(argv[2]); int n_iter = atoi(argv[2]);
// Logic to get/dump the simulation data
const int dump_interval = 5; // dump every 5 iterations -> 20 fps (since dt = 0.01)
const char *dump_path = (argc >= 4) ? argv[3] : nullptr;
std::vector<std::tuple<int, int, double, double, int>> samples; // t, id, x, y, color
if (dump_path)
samples.reserve((size_t)n_iter / dump_interval * n_p);
Pedestrian *pedestrians = init_pedestrians(n_p); Pedestrian *pedestrians = init_pedestrians(n_p);
// # Start here or right after function signature
uint64_t start = start_tsc();
// # (8 + 55N - 55 + 192 + 4 + 8 + 4 + 10)*n_p*n_iter
for (int t = 0; t < n_iter; t++) { for (int t = 0; t < n_iter; t++) {
// Pass1: conmpute all forces from the current state
for (int i = 0; i < n_p; i++) { for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i]; Pedestrian &p = pedestrians[i];
p.f_i.reset(); p.F_i.reset();
// # 8 flops //
// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
//
compute_acceleration_term(&p); compute_acceleration_term(&p);
// # 55N - 55 flops //
// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
//
compute_repulsive_effects_from_pedestrians(pedestrians, &p, n_p); compute_repulsive_effects_from_pedestrians(pedestrians, &p, n_p);
// # 192 flops //
compute_repulsive_effects_from_borders(board, &p); // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
//
compute_repulsive_effects_from_borders(B, &p);
//
// $\vec{w}_i = \vec{v}_i + \vec{F}_idt$
//
p.w_alpha = p.curr_vel + p.F_i * delta_t;
//
// $\vec{v}_i^{t+1} = \vec{w}_ig(\frac{v^{max}_i}{\Arrowvert \vec{w}_i \Arrowvert}) $
//
p.next_vel = p.w_alpha * g(p.v_max, p.w_alpha.norm());
//
// $\vec{r}_i^{t+1} += \vec{r}^t_i + v^t_idt$
//
p.curr_pos = p.curr_pos + p.curr_vel * delta_t;
} }
// Pass2: integrate the forces and update the current state
for (int i = 0; i < n_p; i++) { for (int i = 0; i < n_p; i++) {
Pedestrian &p = pedestrians[i]; pedestrians[i].update();
// # 4 flops = 2 ADDs, 2 MULTs
// integrate preferred velocity: dw/dt = F (Eq. 10)
p.w = p.w + p.f_i * dt;
// Vector2d w_alpha = p.curr_vel + p.f_i * dt; // commented since this was using p.curr_vel instead of p.w (different from paper)
// # 8 flops = 2 MULTs, 2 DIVs, 1 SQRT, 2 MULTs, 1 ADD
p.curr_vel = p.w * g(p.v_max, p.w.norm());
// # 4 flops = 2 ADDs, 2 MULTs
p.curr_pos = p.curr_pos + p.curr_vel * dt;
// how to handle if a pedestrian reached the end of our simulation/end of the hallway
// The option we do here is to just "teleport" the person back to the beginning
// the good thing is we will have a constant flow of pedestrians and in the simulation we should see how lines build up
if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
// basically just keep the y position and reset the x
p.curr_pos.x = 1.0;
p.destination.y = p.curr_pos.y;
}
if (p.color == BLACK && p.curr_pos.x < 1.5) {
p.curr_pos.x = BOARD_LENGTH - 1.0;
p.destination.y = p.curr_pos.y;
}
// # 10 flops = 5 ADDs, 2 DIVs, 1 SQRT, 2 MULTs
p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
// if we have a dump path/file we wanna save the data for visualisation
if (dump_path && (t % dump_interval == 0)) {
samples.emplace_back(t, i, p.curr_pos.x, p.curr_pos.y, p.color);
}
}
}
long long W = (long long)(8 + 55 * n_p - 55 + 192 + 4 + 8 + 4 + 10) * n_p * n_iter;
std::cout << "W(n): ";
std::cout << W;
std::cout << " flops\n";
double T = stop_tsc(start);
std::cout << "T(n): ";
std::cout << T;
std::cout << " cycles\n";
double P = W / T;
std::cout << "P(n): ";
std::cout << P;
std::cout << " flops/cycle\n";
// now we wanna save the data to a csv
if (dump_path) {
std::ofstream out(dump_path);
out << "time,id,x,y,color\n";
for (auto &[t, id, x, y, c] : samples) {
out << t << ',' << id << ',' << x << ',' << y << ',' << c << '\n';
} }
} }
delete[] pedestrians;
return 0; return 0;
} }
pedestrian.hpp
+40 -11
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@@ -2,23 +2,52 @@
#include "random" #include "random"
#include "vector2d.hpp" #include "vector2d.hpp"
// For visual simulation
enum Color { BLACK, WHITE };
struct Pedestrian { struct Pedestrian {
Color color;
Vector2d curr_vel; // v_alpha (actual, ≤ v_max) // TODO: Update initial values
Vector2d w; // w_alpha (preferred, integrates force) Pedestrian() : curr_vel(), curr_pos(), e(), v_max(1.34 * 1.3), desired_v0(1.34), destination(), F_i(), next_pos(), next_vel(), w_alpha() {}
Pedestrian(Vector2d c_v, Vector2d c_p, Vector2d e, Vector2d d, Vector2d f, Vector2d n_p, Vector2d n_v, Vector2d w_alpha) : curr_vel(c_v), curr_pos(c_p), e(e), v_max(), desired_v0(), destination(d), F_i(f), next_pos(n_p), next_vel(n_v), w_alpha(w_alpha) {}
// $\vec{v}_{\alpha}(t)$
Vector2d curr_vel;
// $\vec{r}_\alpha(t)$
Vector2d curr_pos; Vector2d curr_pos;
// $\vec{e}_{\alpha}(t)$
Vector2d e; Vector2d e;
// $v_{\alpha}^{max} = 1.3 v^{0}_{\alpha}$
double v_max; double v_max;
// $v_\alpha^0$
double desired_v0; double desired_v0;
// # =============================================================
// $\vec{r}^0_\alpha$
Vector2d destination; Vector2d destination;
Vector2d f_i;
Pedestrian() : color(WHITE), curr_vel(0.0, 0.0), w(0.0, 0.0), curr_pos(0.0, 0.0), e(0.0, 0.0), v_max(0.0), desired_v0(0.0), destination(0.0, 0.0), f_i(0.0, 0.0) {} // social force
Vector2d F_i;
Pedestrian(Color clr, Vector2d c_v, Vector2d c_p, Vector2d e, double v_max, double desired_v0, Vector2d d, Vector2d f) // # =============================================================
: color(clr), curr_vel(c_v), w(c_v), // initialize w = v at t=0
curr_pos(c_p), e(e), v_max(v_max), desired_v0(desired_v0), destination(d), f_i(f) {} // $\vec{r}_\alpha(t+1)$
Vector2d next_pos;
// $\vec{v}_{\alpha}(t + 1)$
Vector2d next_vel;
// $\vec{w}_{\alpha}$
Vector2d w_alpha;
// # =============================================================
void update() {
curr_pos = next_pos;
curr_vel = next_vel;
e = (destination - curr_pos) / (destination - curr_pos).norm();
}
}; };