Merge remote-tracking branch 'origin/smail' into lbaumberger
This commit is contained in:
lbaumberger
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# Advanced Systems Lab - Spring 2026
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This is the GitLab repository of group 70.
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Project topic: Social Force Model for Pedestrian Dynamics
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||||
The goal of this project was to implement a fast version of Helbing's social force model. The final report includes an analysis and results of the work.
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This repository contains all C++ and Python code that was used to generate and analyze the data.
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||||
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||||
## Compilation
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||||
Linux
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||||
|
||||
``
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||||
|
||||
Windows
|
||||
|
||||
``
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||||
|
||||
Apple
|
||||
|
||||
`clang++ -O3 -march=native -o main_test_visualisation main.cpp forces.cpp`
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||||
Compile the code with running the bash script 'src/main/run_simulation.sh'. After compilation was succesfull the user has 3 different option:
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||||
1. Benchmark
|
||||
2. Test
|
||||
3. Visualization
|
||||
|
||||
The flags used are:
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||||
`gcc -lm -O3 -mavx2 -mfma -ffast-math -g`
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||||
## Storing coordinates in a csv
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||||
`./main_test_visualisation <number of pedestrians> <number of iterations> <csv file>`
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||||
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||||
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||||
## Visualization
|
||||
`python3 visualization.py <csv file> <mp4 file>`
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||||
|
||||
## Benchmark
|
||||
|
||||
|
||||
|
||||
## Test
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|
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+49
-35
@@ -8,15 +8,22 @@ double w(Vector2d e, Vector2d f) {
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return c;
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}
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// # Done
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||||
// # 2 DIVs
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double g(double v_max_alpha, double w_alpha_norm) {
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||||
if (w_alpha_norm <= v_max_alpha) {
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return 1;
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}
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return v_max_alpha / w_alpha_norm;
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}
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// # 8 flops
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//
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// $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) \vec{e}_\alpha(t) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
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//
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void compute_acceleration_term(Pedestrian *p) {
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// # 2 flops
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// $v_{\alpha}^{0}\vec{e}_{\alpha}$
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Vector2d desired_velocity = p->e * p->desired_v0;
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|
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// $\vec{v}_{\alpha}(t)$
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Vector2d actual_velocity = p->curr_vel;
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||||
@@ -25,11 +32,10 @@ void compute_acceleration_term(Pedestrian *p) {
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p->F_i += (desired_velocity - actual_velocity) * tau_alpha_inv;
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}
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|
||||
// # Done
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||||
// # 55(N - 1) = 55N - 55 flops
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//
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// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
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//
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void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestrian *p, int N) {
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||||
|
||||
for (int i = 0; i < N; i++) {
|
||||
@@ -43,56 +49,58 @@ void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestr
|
||||
// continue;
|
||||
// }
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||||
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||||
// # 2 ADDs
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// $\vec{r}_{\alpha\beta}$
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Vector2d r_ab = p->curr_pos - pedestrians[i].curr_pos;
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// # 4 flops = 1 SQRT, 2 MULTs, 1 ADD
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// $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
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double r_ab_norm = r_ab.norm();
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|
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// $\vec{e}_{\beta}$
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Vector2d e_beta = pedestrians[i].e;
|
||||
Vector2d e_b = pedestrians[i].e;
|
||||
|
||||
// # 2 MULTs
|
||||
// $v_\beta \Delta t$
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||||
double v_beta_delta_t = pedestrians[i].desired_v0 * delta_t;
|
||||
double s_b = pedestrians[i].curr_vel.norm() * delta_t;
|
||||
|
||||
// # 4 flops = 2 ADDs, 2 MULTs
|
||||
// $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
|
||||
Vector2d r_ab_minus_v_beta_delta_t_e_beta = r_ab - (e_beta * v_beta_delta_t);
|
||||
Vector2d r_ab2 = r_ab - (e_b * s_b);
|
||||
|
||||
// # 4 flops = 1 SQRT, 2 MULTs, 1 ADD
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||||
// $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
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||||
double r_ab_minus_v_beta_delta_t_e_beta_norm = r_ab_minus_v_beta_delta_t_e_beta.norm();
|
||||
double r_ab2_norm = r_ab2.norm();
|
||||
|
||||
// (4):
|
||||
// # 7 flops = 1 SQRT, 3 ADDs, 2 MULTs, 1 DIV
|
||||
//
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||||
// $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
|
||||
// $(4):2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
|
||||
//
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||||
double b = sqrt((r_ab_norm + r_ab2_norm) * (r_ab_norm + r_ab2_norm) - s_b * s_b) / 2;
|
||||
|
||||
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;
|
||||
// # 12 flops = 6 DIVs, 3 ADDs, 3 MULTs
|
||||
//
|
||||
// $(7):\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert) (\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{4b}$
|
||||
//
|
||||
Vector2d b_grad = (r_ab / r_ab_norm + r_ab2 / r_ab2_norm) * (r_ab_norm + r_ab2_norm) / (4 * b);
|
||||
|
||||
// (7):
|
||||
// # 6 flops = 3 MULTs, 2 DIVs, 1 EXP
|
||||
//
|
||||
// $\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert) (\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{4b}$
|
||||
// $(5):\vec{f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vec{r}_{\alpha \beta}}b$
|
||||
//
|
||||
//
|
||||
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);
|
||||
Vector2d f_ped = b_grad * ((V0_AB / SIGMA) * (exp(-b / SIGMA)));
|
||||
|
||||
// (5):
|
||||
// # 14 flops: 9 MULTs, 4 ADDs, 1 SQRT
|
||||
//
|
||||
// $\vec{f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vec{r}_{\alpha \beta}}b$
|
||||
// $(11):\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta}) = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta})\vec{f}_{\alpha\beta}(\vec{r}_{\alpha} - \vec{r}_{\beta})$
|
||||
//
|
||||
//
|
||||
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);
|
||||
p->f_i += f_ped * w(p->e, f_ped * -1);
|
||||
}
|
||||
}
|
||||
|
||||
// # Done
|
||||
void compute_repulsive_effects_from_borders(Board &B, Pedestrian *p) {
|
||||
// # Assume that the four borders are also 4 obstacles
|
||||
// # 48 n_obstacles flops = 192 flops
|
||||
void compute_repulsive_effects_from_borders(Board &board, Pedestrian *p) {
|
||||
|
||||
Vector2d *borders = B.borders;
|
||||
int n_borders = B.n_borders;
|
||||
@@ -101,20 +109,26 @@ void compute_repulsive_effects_from_borders(Board &B, Pedestrian *p) {
|
||||
|
||||
Vector2d border = borders[i];
|
||||
|
||||
// # 17 flops
|
||||
// $\vec{r}_{\alpha B}$
|
||||
Vector2d r_alpha_B = p->curr_pos - border;
|
||||
Vector2d r_aB = p->curr_pos - obstacle.find_nearest_obstacle_point(p->curr_pos);
|
||||
|
||||
// # 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
|
||||
//
|
||||
// $\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
|
||||
//
|
||||
Vector2d f_obst = (r_aB / r_aB.norm()) * (U0_AB / R) * std::exp(-r_aB.norm() / R);
|
||||
|
||||
Vector2d F_alpha_B = (r_alpha_B / r_alpha_B.norm()) * (U0_AB / R) * std::exp(-r_alpha_B.norm() / R);
|
||||
// # 2 ADDs
|
||||
// p->f_i += f_obst; //commented since this version dont takes the field of view / angle to the border into account
|
||||
|
||||
// (12):
|
||||
// I have read the paper again and on page 6 they say
|
||||
// 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)
|
||||
// # 14 flops: 9 MULTs, 4 ADDS, 1 SQRT
|
||||
//
|
||||
// $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{ B}) = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B})\vec{F}_{\alpha B}(\vec{r}_{\alpha} - \vec{r}_{ B})$
|
||||
// $(12):\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{ B}) = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B})\vec{F}_{\alpha B}(\vec{r}_{\alpha} - \vec{r}_{ B})$
|
||||
//
|
||||
|
||||
p->F_i += F_alpha_B * w(p->e, F_alpha_B * (-1));
|
||||
p->f_i += f_obst * w(p->e, f_obst * -1);
|
||||
}
|
||||
}
|
||||
|
||||
+3
-9
@@ -4,40 +4,34 @@
|
||||
#include <cmath>
|
||||
|
||||
// $\tau_{\alpha}$
|
||||
// https://arxiv.org/pdf/cond-mat/9805244, page 8
|
||||
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;
|
||||
|
||||
// $\sigma$
|
||||
// https://arxiv.org/pdf/cond-mat/9805244, page 8
|
||||
const double SIGMA = 0.3;
|
||||
|
||||
// $U^0_{\alpha B}$
|
||||
// https://arxiv.org/pdf/cond-mat/9805244, page 8
|
||||
const double U0_AB = 10.0;
|
||||
|
||||
// $R$
|
||||
// https://arxiv.org/pdf/cond-mat/9805244, page 8
|
||||
const double R = 0.2;
|
||||
|
||||
// $c$
|
||||
// https://arxiv.org/pdf/cond-mat/9805244, page 8
|
||||
const double c = 0.5;
|
||||
|
||||
// $\varphi$
|
||||
// https://arxiv.org/pdf/cond-mat/9805244, page 8
|
||||
const double phi = 100.0;
|
||||
const double phi = 100.0 / 180.0 * M_PI;
|
||||
|
||||
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 dt = 0.001;
|
||||
|
||||
// # Optional (forces.cpp: compute_repulsive_effects_from_pedestrians)
|
||||
// const double DISTANCE = 2.0;
|
||||
|
||||
|
||||
@@ -41,6 +41,14 @@ int main(int argc, const char *argv[]) {
|
||||
int n_p = atoi(argv[1]);
|
||||
int n_iter = atoi(argv[2]);
|
||||
|
||||
// Logic to get/dump the simulation data
|
||||
const int dump_interval = 50; // 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);
|
||||
|
||||
for (int t = 0; t < n_iter; t++) {
|
||||
@@ -51,41 +59,88 @@ int main(int argc, const char *argv[]) {
|
||||
|
||||
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);
|
||||
|
||||
// # 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);
|
||||
|
||||
// # 192 flops
|
||||
//
|
||||
// $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
|
||||
//
|
||||
compute_repulsive_effects_from_borders(B, &p);
|
||||
compute_repulsive_effects_from_borders(board, &p);
|
||||
}
|
||||
// Pass2: integrate the forces and update the current state
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
Pedestrian &p = pedestrians[i];
|
||||
// # 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)
|
||||
|
||||
//
|
||||
// $\vec{w}_i = \vec{v}_i + \vec{F}_idt$
|
||||
//
|
||||
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());
|
||||
// # 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
|
||||
//
|
||||
// $\vec{r}_i^{t+1} += \vec{r}^t_i + v^t_idt$
|
||||
//
|
||||
p.curr_pos = p.curr_pos + p.curr_vel * delta_t;
|
||||
}
|
||||
p.curr_pos = p.curr_pos + p.curr_vel * dt;
|
||||
|
||||
for (int i = 0; i < n_p; i++) {
|
||||
pedestrians[i].update();
|
||||
// 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;
|
||||
}
|
||||
|
||||
Regular → Executable
BIN
Binary file not shown.
+9
-33
@@ -3,51 +3,27 @@
|
||||
#include "vector2d.hpp"
|
||||
|
||||
struct Pedestrian {
|
||||
|
||||
// TODO: Update initial values
|
||||
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) {}
|
||||
|
||||
Color color;
|
||||
// $\vec{v}_{\alpha}(t)$
|
||||
Vector2d curr_vel;
|
||||
|
||||
Vector2d curr_vel; // v_alpha (actual, ≤ v_max)
|
||||
// $\vec{w}_{\alpha}$
|
||||
Vector2d w; // w_alpha (preferred, integrates force)
|
||||
// $\vec{r}_\alpha(t)$
|
||||
Vector2d curr_pos;
|
||||
|
||||
// $\vec{e}_{\alpha}(t)$
|
||||
Vector2d e;
|
||||
|
||||
// $v_{\alpha}^{max} = 1.3 v^{0}_{\alpha}$
|
||||
double v_max;
|
||||
|
||||
// $v_\alpha^0$
|
||||
double desired_v0;
|
||||
|
||||
// # =============================================================
|
||||
|
||||
// $\vec{r}^0_\alpha$
|
||||
Vector2d destination;
|
||||
|
||||
// social force
|
||||
Vector2d F_i;
|
||||
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) {}
|
||||
|
||||
// $\vec{r}_\alpha(t+1)$
|
||||
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();
|
||||
}
|
||||
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) {}
|
||||
};
|
||||
|
||||
+999999
-999999
File diff suppressed because it is too large
Load Diff
Binary file not shown.
Reference in New Issue
Block a user