car_sim_ring_IV24.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fe1dab1-1eeb-4570-a4de-969dce9cdd52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "import pandas as pd\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0e9fe4b-73b7-495b-834b-0220fbec3232",
   "metadata": {},
   "outputs": [],
   "source": [
    "def IDM_Accel(params, v, v_l, s):\n",
    "    # Returns the acceleration of the IDM for a particular parameter set and\n",
    "    # position/speed condition:\n",
    "\n",
    "    v_0 = params[0]  # Desired Speed Guess 20\n",
    "    T = params[1]  # Desired Time-gap  2\n",
    "    s_0 = params[2]  # Minimum Gap   4\n",
    "    delta = params[3]  # Acceleration Exponent 2\n",
    "    a = params[4]  # max Acceleration rate 2\n",
    "    b = params[5]  # Comfortable Deceleration 3\n",
    "\n",
    "    delta_V = v_l - v\n",
    "\n",
    "    s_star = s_0 + max([0.0, v*T - (v*delta_V) / (2 * np.sqrt(a * b))])\n",
    "\n",
    "    accel = a * (1 - (v / v_0) ** delta - (s_star / s) ** 2)  \n",
    "\n",
    "\n",
    "    return accel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95fadc88-a746-411a-b838-7671398dc16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def OVRV_Accel(params, v, v_l, s):\n",
    "    # Returns the acceleration of the OVRV for a particular parameter set and\n",
    "    # position/speed condition:\n",
    "\n",
    "    k1 = params[0]\n",
    "    k2 = params[1]\n",
    "    eta = params[2]\n",
    "    tau = params[3]\n",
    "\n",
    "    delta_V = v_l - v\n",
    "\n",
    "\n",
    "    accel = k1 * (s - eta - tau * v) + k2 * (v_l - v)\n",
    "\n",
    "\n",
    "\n",
    "    return accel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e19e0c3-98fc-458e-8852-c1f548783010",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeSim_ring_mix(numSteps, numVeh, params_IDM,params_OVRV, delta_T, initVel_Follower, initGap, AttackedVeh):\n",
    "    spacing_gap_Profile = np.zeros((numSteps, numVeh))\n",
    "    spacing_gap_Profile[0, :] = np.squeeze(initGap)\n",
    "    velProfile_Follower = np.zeros((numSteps, numVeh))\n",
    "    velProfile_Follower[0, :] = initVel_Follower\n",
    "    accelVal = np.zeros((numSteps, numVeh))\n",
    "\n",
    "    sigma = 0.5\n",
    "\n",
    "    for t in range(1, numSteps):\n",
    "        for i in range(numVeh):\n",
    "            noise = norm.rvs(0, sigma)\n",
    "            # noise_attack = norm.rvs(0, 5)\n",
    "\n",
    "            if i == 0:  # Leader\n",
    "                accelVal[t - 1, i] = IDM_Accel(params_IDM[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, numVeh - 1], spacing_gap_Profile[t - 1, i]) + noise\n",
    "                # accelVal[t - 1, i] = max(accelVal[t - 1, i], -2)\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, numVeh - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "\n",
    "            elif AttackedVeh[0, i] == 2:  # Attack Vehicles in AttackedVeh Matrix\n",
    "                accelVal[t - 1, i] = OVRV_Accel(params_OVRV[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i])\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "\n",
    "            elif AttackedVeh[0, i] == 1:\n",
    "                accelVal[t - 1, i] = OVRV_Accel(params_OVRV[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i])\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "            \n",
    "            else:\n",
    "                accelVal[t - 1, i] = IDM_Accel(params_IDM[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i])\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "\n",
    "    return spacing_gap_Profile, velProfile_Follower, accelVal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3def9d1c-af0b-4ab2-b134-17db86854be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def OVRV_Accel_attack(params, v, v_l, s,slope_1, slope_2):\n",
    "\n",
    "\n",
    "    k1 = params[0]\n",
    "    k2 = params[1]\n",
    "    eta = params[2]\n",
    "    tau = params[3]\n",
    "\n",
    "    delta_V = v_l - v\n",
    "    attack_spacing = slope_1*(s); #%attack on spacing\n",
    "    attack_speed_relative = slope_2*(delta_V);  #attack on relative speed\n",
    "\n",
    "    accel = k1 * (s+ attack_spacing- eta - tau * v) + k2 * (v_l - v+attack_speed_relative)\n",
    "\n",
    "\n",
    "\n",
    "    return accel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "870c5816-40b1-4741-b3da-6ba9d3b99e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeSim_ring_mix_attack(numSteps, numVeh, params_IDM,params_OVRV, delta_T, initVel_Follower, initGap, AttackedVeh,attack_start, attack_end,slope_1, slope_2):\n",
    "    spacing_gap_Profile = np.zeros((numSteps, numVeh))\n",
    "    spacing_gap_Profile[0, :] = np.squeeze(initGap)\n",
    "    velProfile_Follower = np.zeros((numSteps, numVeh))\n",
    "    velProfile_Follower[0, :] = initVel_Follower\n",
    "    accelVal = np.zeros((numSteps, numVeh))\n",
    "\n",
    "    sigma = 0.5\n",
    "\n",
    "    for t in range(1, numSteps):\n",
    "        for i in range(numVeh):\n",
    "            noise = norm.rvs(0, sigma)\n",
    "            noise_attack = norm.rvs(0, 5)\n",
    "\n",
    "            if i == 0:  # Leader\n",
    "                accelVal[t - 1, i] = IDM_Accel(params_IDM[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, numVeh - 1], spacing_gap_Profile[t - 1, i]) + noise\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, numVeh - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "\n",
    "            elif AttackedVeh[0, i] == 2:\n",
    "                if attack_start <= t < attack_end:  # Attack Vehicles within specific time\n",
    "                    # Behavior when the time is within the attack range\n",
    "                    accelVal[t - 1, i] = OVRV_Accel_attack(params_OVRV[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i], slope_1, slope_2)\n",
    "                    accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                    velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                    spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "                else:\n",
    "                    # Behavior when the time is outside the attack range - treat as AttackedVeh == 1\n",
    "                    accelVal[t - 1, i] = OVRV_Accel(params_OVRV[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i])\n",
    "                    accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                    velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                    spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "\n",
    "\n",
    "            elif AttackedVeh[0, i] == 1:\n",
    "                accelVal[t - 1, i] = OVRV_Accel(params_OVRV[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i])\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "            \n",
    "            else:\n",
    "                accelVal[t - 1, i] = IDM_Accel(params_IDM[i, :], velProfile_Follower[t - 1, i], velProfile_Follower[t - 1, i - 1], spacing_gap_Profile[t - 1, i])\n",
    "                accelVal[t - 1, i] = max(min(accelVal[t - 1, i], 2), -5)\n",
    "                velProfile_Follower[t, i] = max(velProfile_Follower[t - 1, i] + accelVal[t - 1, i] * delta_T, 0.5)\n",
    "                spacing_gap_Profile[t, i] = max(spacing_gap_Profile[t - 1, i] + (velProfile_Follower[t - 1, i - 1] - velProfile_Follower[t - 1, i]) * delta_T, 0.5)\n",
    "\n",
    "    return spacing_gap_Profile, velProfile_Follower, accelVal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab2636e2-2f87-4b37-a86d-01eb7d8651da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Configuration Parameters\n",
    "L = 300  # Length of the ring in meters\n",
    "numVeh = 10  # Number of vehicles in the ring\n",
    "numSteps = 30 * 60 * 2  # Number of simulation steps (30 minutes)\n",
    "deltaT = 1 / 30  # Time step in seconds\n",
    "time_series = np.arange(0, numSteps * deltaT, deltaT)  # Array of time steps\n",
    "car_length = 5  # Length of each car in meters\n",
    "\n",
    "# Car-Following Parameters\n",
    "ACC_params = [44.1, 2.2, 6.3, 15.5, 0.6, 5.2]  # Parameters for commercial available ACC\n",
    "Human_params = [30, 1.26, 3.4, 4, 1.06, 2]  # Parameters for human drivers\n",
    "Theoretical_ACC_params = [33.33, 1.5, 2, 4, 1.4, 2]  # Theoretical ACC parameters\n",
    "\n",
    "# ACC Configuration\n",
    "eta = 0.5  # ACC-related parameter (adjust as needed)\n",
    "ACC_indices = [1]  # Indices of ACC-equipped vehicles\n",
    "Attack_indices = [1]  # Indices of attacked ACC-equipped vehicles\n",
    "\n",
    "# Initializing Car Parameters\n",
    "IDM_parameters = np.tile(Human_params, (numVeh, 1))  # IDM parameters for each vehicle\n",
    "\n",
    "# Initial States\n",
    "attacked_Vehicles = np.zeros((1, numVeh))\n",
    "total_vehicle_length = car_length * numVeh\n",
    "initial_spacing = (L - total_vehicle_length) / numVeh  # Initial spacing between vehicles\n",
    "S_E = np.full((numVeh, 1), initial_spacing)\n",
    "initial_velocity = np.zeros((1, numVeh))\n",
    "\n",
    "# Updating Vehicle States for ACC and Attacked Vehicles\n",
    "attacked_Vehicles[0, ACC_indices] = 1  # Mark ACC vehicles\n",
    "attacked_Vehicles[0, Attack_indices] = 2  # Mark attacked ACC vehicles\n",
    "IDM_parameters[ACC_indices, :] = ACC_params  # Update IDM parameters for ACC vehicles\n",
    "\n",
    "# Overriding ACC Parameters\n",
    "ACC_override_params = [0.02, 0.13, 21.51, 1.71]  # ACC override parameters\n",
    "OVRV_parameters = np.tile(ACC_override_params, (numVeh, 1))\n",
    "\n",
    "# Attack Simulation Configuration\n",
    "attack_start_time = 30 * 60 * 1  # Start time of the attack\n",
    "attack_end_time = 30 * 60 * 1.5  # End time of the attack\n",
    "\n",
    "# Attack Slope Parameters\n",
    "slope_params = [-0.1, -0.3, -0.5, -0.7, -0.9, -2, 2, 5, -5, -3, -1.5]\n",
    "slope_1 = slope_params[2]\n",
    "slope_2 = slope_params[2]\n",
    "\n",
    "# Running the Simulation\n",
    "simSpacing, simFollowVel, simAcc = makeSim_ring_mix_attack(numSteps, numVeh, IDM_parameters, OVRV_parameters, deltaT, initial_velocity, S_E, attacked_Vehicles, attack_start_time, attack_end_time, slope_1, slope_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f67df5e6-3145-45bc-9b69-fdaff511b51a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping and accumulating spacing data\n",
    "last_vehicle_spacing = simSpacing[:, numVeh - 1]  # Extract spacing for the last vehicle\n",
    "remaining_vehicle_spacing = simSpacing[:, :numVeh - 1]  # Extract spacing for all but the last vehicle\n",
    "# Rearrange spacing data so that the last vehicle's data comes first\n",
    "simSpacing_reshaped = np.hstack((last_vehicle_spacing[:, None], remaining_vehicle_spacing))\n",
    "# Cumulative sum of the rearranged spacing data\n",
    "cumulative_spacing = np.cumsum(simSpacing_reshaped, axis=1)\n",
    "\n",
    "# Ensure all following velocities are non-negative\n",
    "non_negative_follow_vel = np.maximum(simFollowVel, 0)\n",
    "# Extract first vehicle's velocity and calculate its travel distance\n",
    "first_vehicle_vel = non_negative_follow_vel[:, 0]\n",
    "first_vehicle_travel_dist = first_vehicle_vel * deltaT\n",
    "cumulative_first_vehicle_dist = np.cumsum(first_vehicle_travel_dist)\n",
    "# Replicate this distance for all vehicles\n",
    "all_vehicle_cumulative_dist = np.tile(cumulative_first_vehicle_dist, (numVeh, 1)).T\n",
    "\n",
    "# Calculate positions for vehicles 2 to N(e.g., 20) based on distances and spacing\n",
    "position_vehicles_2_to_20 = all_vehicle_cumulative_dist - cumulative_spacing\n",
    "first_vehicle_position = position_vehicles_2_to_20[:, numVeh - 1]\n",
    "remaining_vehicles_position = position_vehicles_2_to_20[:, :numVeh - 1]\n",
    "# Combine the positions of the first vehicle and remaining vehicles\n",
    "combined_positions = np.hstack((first_vehicle_position[:, None], remaining_vehicles_position))\n",
    "\n",
    "# Calculate travel distance based on velocity for all vehicles\n",
    "travel_distance_all_vehicles = simFollowVel * deltaT\n",
    "cumulative_travel_distance_all_vehicles = np.cumsum(travel_distance_all_vehicles, axis=0)\n",
    "\n",
    "# Initialize parameters for position calculation\n",
    "initial_position = L  # starting number\n",
    "spacing_step_length = S_E[0]  # step length (s_e from the above code)\n",
    "number_of_vehicles = numVeh  # number of vehicles\n",
    "# Calculate initial position for each vehicle\n",
    "initial_vehicle_positions = initial_position - spacing_step_length * np.arange(number_of_vehicles)\n",
    "# Replicate initial positions for each time step\n",
    "initial_positions_all_steps = np.tile(initial_vehicle_positions, (numSteps, 1))\n",
    "\n",
    "# Calculate new positions by adding travel distances to initial positions\n",
    "pos_new = cumulative_travel_distance_all_vehicles + initial_positions_all_steps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0af70752-8838-42f9-841f-925fded1ca00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "ax.plot(t, pos_new, 'g.', markersize=0.3, alpha=0.5, label='All vehicles') # Add label\n",
    "ax.plot(t, pos_new[:,ACC_ind], 'bs', markersize=0.3, alpha=0.5, label='ACC vehicles') # Add label\n",
    "# ax.plot(t, pos_new[:,Attack_ind], 'r', markersize=0.3, alpha=0.5, label='ACC vehicles') # Add label\n",
    "\n",
    "ax.set_xlabel(\"Time (sec)\")\n",
    "ax.set_ylabel(\"Position (m)\")\n",
    "\n",
    "# ax.legend() # Add legend\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e59c8d6f-64e6-46d9-b495-f0ac661d95f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 9))\n",
    "\n",
    "# Set the start (a) and end (b) times for the shaded area\n",
    "a = 60# Example start time\n",
    "b = 90 # Example end time\n",
    "\n",
    "# Increase marker size for better visibility in the plot\n",
    "marker_size = 1\n",
    "\n",
    "# Use a darker shade of green for better visibility and less brightness\n",
    "ax.plot(t, pos_new % L, '.', color='#008000', markersize=marker_size, label='Human-driven vehicles')\n",
    "# ax.plot(t, pos_new[:, ACC_ind] %L, 'bs', markersize=marker_size, label='ACC vehicles')\n",
    "plt.plot(t, pos_new[:, Attack_ind] %L, 'r.', markersize=1, alpha=0.5, label=\"Attacked Vehicles\")\n",
    "\n",
    "# The attacked vehicles are not labeled and hence not in the legend\n",
    "\n",
    "ax.set_xlabel(\"Time (sec)\", fontsize=18)  # Increase font size for the axis label\n",
    "ax.set_ylabel(\"Position (m)\", fontsize=18)  # Increase font size for the axis label\n",
    "\n",
    "# Increase font size of the numbers on the axes\n",
    "ax.tick_params(axis='both', labelsize=16)  # Increase font size for the ticks\n",
    "\n",
    "# Add gridlines to the plot\n",
    "ax.grid(True)\n",
    "\n",
    "# Set the legend to show with a larger font, and only for human-driven and ACC vehicles\n",
    "# Since we have multiple HDVs but want them to appear only once in the legend,\n",
    "# we can create custom legend entries:\n",
    "from matplotlib.lines import Line2D\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color='#008000', marker='.', linestyle='None', markersize=10, label='Human-driven vehicles'),\n",
    "    # Line2D([0], [0], color='b', marker='.', linestyle='None', markersize=10, label='ACC vehicles'),\n",
    "        Line2D([0], [0], color='r', marker='.', linestyle='None', markersize=10, label=\"Attacked Vehicles\"),\n",
    "\n",
    "]\n",
    "ax.legend(handles=legend_elements, fontsize=16, loc='lower right', markerscale=2)  # Adjust legend position, font size, and marker scale\n",
    "\n",
    "# Add a title to the plot\n",
    "ax.set_title(\"Vehicle Trajectories Over Time\", fontsize=20)  # Increase title font size\n",
    "ax.axvspan(a, b, color='grey', alpha=0.3)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39461d34-ad5c-45a5-8caf-effd96ba599b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(12, 9))\n",
    "# Set the start (a) and end (b) times for the shaded area\n",
    "a = 60# Example start time\n",
    "b = 90 # Example end time\n",
    "\n",
    "# Plot all vehicles in green with alpha for transparency\n",
    "plt.plot(t, simSpacing, 'g.', markersize=1, alpha=0.5, label=\"Human Vehicles\")\n",
    "\n",
    "# Plot ACC vehicles in blue with alpha for transparency\n",
    "# plt.plot(t, simSpacing[:, ACC_ind], 'b.', markersize=1, alpha=0.5, label=\"ACC Vehicles\")\n",
    "plt.plot(t, simSpacing[:, Attack_ind], 'r.', markersize=1, alpha=0.5, label=\"Attacked Vehicles\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"Time (sec)\", fontsize=18)\n",
    "plt.ylabel(\"Spacing (m)\", fontsize=18)\n",
    "\n",
    "# Increase font size for the tick labels\n",
    "plt.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Add gridlines to the plot for better readability\n",
    "plt.grid(True)\n",
    "\n",
    "# Make top and right spines visible to emulate Matlab's box on\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(True)\n",
    "ax.spines['right'].set_visible(True)\n",
    "\n",
    "# Create custom legend entries with increased marker size\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color='g', marker='.', linestyle='None', markersize=10, label=\"Human Vehicles\"),\n",
    "    # Line2D([0], [0], color='b', marker='.', linestyle='None', markersize=10, label=\"ACC Vehicles\"),\n",
    "    Line2D([0], [0], color='r', marker='.', linestyle='None', markersize=10, label=\"Attacked Vehicles\"),\n",
    "]\n",
    "ax.axvspan(a, b, color='grey', alpha=0.3)\n",
    "\n",
    "# Add a legend to the plot with custom markers\n",
    "ax.legend(handles=legend_elements, fontsize=16, loc='lower right', markerscale=2)  # Adjust legend position, font size, and marker scale\n",
    "\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9405c3ea-35cc-40aa-9387-b85b6ee255de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(12, 9))\n",
    "# Set the start (a) and end (b) times for the shaded area\n",
    "a = 60# Example start time\n",
    "b = 90 # Example end time\n",
    "\n",
    "# Plot all vehicles in green with alpha for transparency\n",
    "plt.plot(t, simAcc, 'g.', markersize=1, alpha=0.5, label=\"Human Vehicles\")\n",
    "\n",
    "# Plot ACC vehicles in blue with alpha for transparency\n",
    "# plt.plot(t, simAcc[:, ACC_ind], 'b.', markersize=1, alpha=0.5, label=\"ACC Vehicles\")\n",
    "\n",
    "# Plot attacked vehicles in red with alpha for transparency\n",
    "plt.plot(t, simAcc[:, Attack_ind], 'r.', markersize=1, alpha=0.5, label=\"Attack Vehicles\")\n",
    "\n",
    "# Set labels with increased font size\n",
    "plt.xlabel(\"Time (sec)\", fontsize=18)\n",
    "plt.ylabel(\"Acceleration (m/s²)\", fontsize=18)\n",
    "\n",
    "# Increase font size for the tick labels\n",
    "plt.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Add gridlines to the plot for better readability\n",
    "plt.grid(True)\n",
    "\n",
    "# Make top and right spines visible to emulate Matlab's box on\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(True)\n",
    "ax.spines['right'].set_visible(True)\n",
    "\n",
    "# Create custom legend entries with increased marker size\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color='g', marker='.', linestyle='None', markersize=10, label=\"Human Vehicles\"),\n",
    "    Line2D([0], [0], color='b', marker='.', linestyle='None', markersize=10, label=\"ACC Vehicles\"),\n",
    "    # Line2D([0], [0], color='r', marker='.', linestyle='None', markersize=10, label=\"Attacked Vehicles\"),\n",
    "]\n",
    "ax.axvspan(a, b, color='grey', alpha=0.3)\n",
    "\n",
    "\n",
    "# Add a legend to the plot with custom markers\n",
    "ax.legend(handles=legend_elements, fontsize=16, loc='lower right', markerscale=2)  # Adjust legend position, font size, and marker scale\n",
    "\n",
    "\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb42220-b564-4aec-bae8-4bc48b442788",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(12, 9))\n",
    "\n",
    "# Set the start (a) and end (b) times for the shaded area\n",
    "a = 60# Example start time\n",
    "b = 90 # Example end time\n",
    "\n",
    "# Plot all vehicles in green with alpha for transparency\n",
    "plt.plot(t, simFollowVel, 'g.', markersize=1, alpha=0.5, label=\"Human Vehicles\")\n",
    "\n",
    "# Plot ACC vehicles in blue with alpha for transparency\n",
    "# plt.plot(t, simFollowVel[:, ACC_ind], 'b.', markersize=1, alpha=0.5, label=\"ACC Vehicles\")\n",
    "\n",
    "# Plot attacked vehicles in red with alpha for transparency\n",
    "plt.plot(t, simFollowVel[:, Attack_ind], 'r.', markersize=1, alpha=0.5, label=\"Attacked Vehicles\")\n",
    "\n",
    "# Set labels with increased font size\n",
    "plt.xlabel(\"Time (sec)\", fontsize=18)\n",
    "plt.ylabel(\"Speed (m/s)\", fontsize=18)\n",
    "\n",
    "# Increase font size for the tick labels\n",
    "plt.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Add gridlines to the plot for better readability\n",
    "plt.grid(True)\n",
    "\n",
    "# Make top and right spines visible to emulate Matlab's box on\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(True)\n",
    "ax.spines['right'].set_visible(True)\n",
    "\n",
    "# Create custom legend entries with increased marker size\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color='g', marker='.', linestyle='None', markersize=10, label=\"Human Vehicles\"),\n",
    "    Line2D([0], [0], color='b', marker='.', linestyle='None', markersize=10, label=\"ACC Vehicles\"),\n",
    "    # Line2D([0], [0], color='r', marker='.', linestyle='None', markersize=10, label=\"Attacked Vehicles\"),\n",
    "]\n",
    "ax.axvspan(a, b, color='grey', alpha=0.3)\n",
    "\n",
    "# Add a legend to the plot with custom markers\n",
    "ax.legend(handles=legend_elements, fontsize=16, loc='lower right', markerscale=2)  # Adjust legend position, font size, and marker scale\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "974acfce-a408-432a-8992-31852ed4e706",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e88fa065-5a1d-4f41-804b-d30961595613",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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