CarFollowingK1D.ipynb

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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
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       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
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       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
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       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
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       "\n",
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       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
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       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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       "  }\n",
       "}(window));"
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"866095d1-8b3e-4cb6-a376-fcb041826686\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"866095d1-8b3e-4cb6-a376-fcb041826686\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '866095d1-8b3e-4cb6-a376-fcb041826686' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"866095d1-8b3e-4cb6-a376-fcb041826686\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from filterpy.kalman import KalmanFilter\n",
    "from filterpy.common import Q_discrete_white_noise\n",
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "from bokeh.plotting import figure \n",
    "from bokeh.layouts import row, column\n",
    "from bokeh.models import Label\n",
    "from ipywidgets import interact\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Initializing arrays for plotting\n",
    "timepoints = np.zeros(timesteps)\n",
    "poscar1 = np.zeros(timesteps)\n",
    "poscar1_est = np.zeros(timesteps)\n",
    "poscar1_meas = np.zeros(timesteps)\n",
    "poscar2 = np.zeros(timesteps)\n",
    "car_sep = np.zeros(timesteps)\n",
    "car2_acc = np.zeros(timesteps)\n",
    "poscar2_kf = np.zeros(timesteps)\n",
    "car_sep_kf = np.zeros(timesteps)\n",
    "car2_acc_kf = np.zeros(timesteps)\n",
    "noiserange = np.zeros(timesteps)\n",
    "pdf_proc = np.zeros(timesteps)\n",
    "pdf_meas = np.zeros(timesteps)\n",
    "def InitPlotArrays():    \n",
    "    timepoints.fill(np.nan)\n",
    "    poscar1.fill(np.nan)\n",
    "    poscar1_est.fill(np.nan)\n",
    "    poscar1_meas.fill(np.nan)\n",
    "    poscar2.fill(np.nan)\n",
    "    car_sep.fill(np.nan)\n",
    "    car2_acc.fill(np.nan)\n",
    "    poscar2_kf.fill(np.nan)\n",
    "    car_sep_kf.fill(np.nan)\n",
    "    car2_acc_kf.fill(np.nan)\n",
    "    noiserange.fill(np.nan)\n",
    "    pdf_proc.fill(np.nan)\n",
    "    pdf_meas.fill(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Setup plots\n",
    "InitPlotArrays()\n",
    "\n",
    "# Adding legends\n",
    "p6 = figure(plot_width = 400, plot_height = 120, x_range=[-0.5, 20], y_range=[-1, 5])\n",
    "colors = ['darkslategray', 'gold', 'crimson']\n",
    "p6.rect([0.2, 0.2, 0.2], [4, 2, 0], width=1, height=1, color=colors, \n",
    "              width_units='data', height_units='data')\n",
    "label1 = Label(x=1, y=3.3, x_units='data', y_units='data', text='Leading Car')\n",
    "label2 = Label(x=1, y=1.3, x_units='data', y_units='data', text='Following Car')\n",
    "label3 = Label(x=1, y=-0.7, x_units='data', y_units='data', text='Following Car with Kalman Filter')\n",
    "p6.add_layout(label1)\n",
    "p6.add_layout(label2)\n",
    "p6.add_layout(label3)\n",
    "p6.yaxis.visible = False\n",
    "p6.ygrid.visible = False\n",
    "p6.xaxis.visible = False\n",
    "p6.xgrid.visible = False\n",
    "p6.toolbar.logo = None\n",
    "p6.toolbar_location = None\n",
    "\n",
    "# Figure 1\n",
    "p5 = figure(plot_width = 400, plot_height = 200, title='Noise Distributions')\n",
    "r11 = p5.line(noiserange, pdf_proc, line_color='seagreen', line_width=4, legend='Process')\n",
    "r12 = p5.line(noiserange, pdf_meas, line_color='greenyellow', line_width=4, legend='Measured')\n",
    "p5.toolbar.logo = None\n",
    "p5.toolbar_location = None\n",
    "p5.legend.location = 'bottom_center'\n",
    "\n",
    "# Figure 2\n",
    "p4 = figure(plot_width=1000, plot_height=150, x_axis_label='position', title='Car Following Animation',\n",
    "            x_range=[0, dist_size], y_range=[-0.4, 1.0])\n",
    "colors = ['darkslategray', 'gold', 'crimson']\n",
    "r10 = p4.rect(x=[car1_initpos, car2_initpos, car2_initpos], y=[0.0, -0.2, 0.2], \n",
    "              width=car_size, height=8, color=colors, \n",
    "              width_units='data', height_units='screen')\n",
    "p4.yaxis.visible = False\n",
    "p4.ygrid.visible = False\n",
    "p4.toolbar.logo = None\n",
    "p4.toolbar_location = None\n",
    "\n",
    "# Figure 3\n",
    "p1 = figure(plot_width=500, plot_height=400, x_axis_label='time', y_axis_label='position',\n",
    "            title='Car Positions', x_range=[0, timesteps * dt], y_range=[0, dist_size])\n",
    "r1 = p1.line(timepoints, poscar1, color='darkslategray', line_width=4)\n",
    "r2 = p1.line(timepoints, poscar1_meas, color='royalblue', \n",
    "             legend='Measured position', line_width=2)\n",
    "r3 = p1.line(timepoints, poscar1_est, color='skyblue', \n",
    "             legend='KF estimated position', line_width=2)\n",
    "r4 = p1.line(timepoints, poscar2, color='gold', line_width=4)\n",
    "r5 = p1.line(timepoints, poscar2_kf, color='crimson', line_width=4)\n",
    "p1.legend.location = 'bottom_right'\n",
    "p1.toolbar.logo = None\n",
    "p1.toolbar_location = None\n",
    "\n",
    "# Figure 2\n",
    "p2 = figure(plot_width=500, plot_height=200, x_axis_label='time', y_axis_label='separation',\n",
    "            title='Car Separations', x_range=[0, timesteps * dt], y_range=[0, 4 * dist_sep])\n",
    "r6 = p2.line(timepoints, car_sep, color='gold', line_width=4)\n",
    "r7 = p2.line(timepoints, car_sep_kf, color='crimson', line_width=4)\n",
    "p2.toolbar.logo = None\n",
    "p2.toolbar_location = None\n",
    "\n",
    "# Figure 3\n",
    "p3 = figure(plot_width=500, plot_height=200, x_axis_label='time', y_axis_label='acceleration',\n",
    "            title='Following Car Acceleration', x_range=[0, timesteps * dt], y_range=[-car2_maxbrake, car2_maxacc])\n",
    "r8 = p3.line(timepoints, car2_acc, color='gold', line_width=4)\n",
    "r9 = p3.line(timepoints, car2_acc_kf, color='crimson', line_width=4)\n",
    "p3.toolbar.logo = None\n",
    "p3.toolbar_location = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def RunCarFollowing():\n",
    "    \n",
    "    # Setup initial values\n",
    "    InitPlotArrays()\n",
    "    pos1real = car1_initpos\n",
    "    pos2 = car2_initpos\n",
    "    pos2_kf = car2_initpos\n",
    "    vel2 = car2_initvel\n",
    "    vel2_kf = car2_initvel\n",
    "    acc2 = car2_initacc\n",
    "    acc2_kf = car2_initacc\n",
    "    tt = 0\n",
    "    realt = 0\n",
    "    \n",
    "    # Update noise distributions\n",
    "    if noise_type == 'gauss':\n",
    "        nlim = max(process_noise_level, measurement_noise_level)\n",
    "        global noiserange, pdf_proc, pdf_meas\n",
    "        noiserange = np.linspace(-4 * nlim, 4 * nlim, timesteps)\n",
    "        pdf_proc = (1 / (process_noise_level * np.sqrt(2 * np.pi)) \n",
    "                    * np.exp(-noiserange**2 / (2 * process_noise_level**2)))\n",
    "        r11.data_source.data['x'] = noiserange\n",
    "        r11.data_source.data['y'] = pdf_proc\n",
    "        pdf_meas = (1 / (measurement_noise_level * np.sqrt(2 * np.pi)) \n",
    "                    * np.exp(-noiserange**2 / (2 * measurement_noise_level**2)))\n",
    "        r12.data_source.data['x'] = noiserange\n",
    "        r12.data_source.data['y'] = pdf_meas\n",
    "        push_notebook(handle=target3)\n",
    "    \n",
    "    # Initialize Kalman Filter\n",
    "    f = KalmanFilter(dim_x=2, dim_z=1)\n",
    "    f.x = np.array([[car1_initpos],  # position\n",
    "                    [car1_vel]])  # velocity\n",
    "    f.F = np.array([[1.0, 1.0],\n",
    "                    [0.0, 1.0]])\n",
    "    f.H = np.array([[1.0, 0.0]])\n",
    "    f.P = np.array([[1000.0, 0.0],\n",
    "                    [0.0, 1000.0]])\n",
    "    f.R = np.array([[5.0]])\n",
    "    f.Q = Q_discrete_white_noise(dim=2, dt=dt, var=process_noise_level**2)  # Process noise\n",
    "    \n",
    "    # Loop for updating positions\n",
    "    for tt in range(timesteps):\n",
    "\n",
    "        # Real position of leading car\n",
    "        pos1real = pos1real + car1_vel * dt\n",
    "\n",
    "        # Measurement of position of leading car with noise\n",
    "        if noise_type == 'gauss':\n",
    "            z = np.random.normal(pos1real, measurement_noise_level)\n",
    "        elif noise_type == 'power':\n",
    "            z = (pos1real + 2.0 * measurement_noise_level \n",
    "                 * np.sign((np.random.uniform() - 0.5)) * np.random.power(5))\n",
    "        elif noise_type == 'uniform':\n",
    "            z = pos1real + 4.0 * measurement_noise_level * (np.random.uniform() - 0.5)\n",
    "        else:\n",
    "            z = pos1real\n",
    "\n",
    "        # Kalman Filter predict and update steps\n",
    "        f.predict()\n",
    "        f.update(z)   \n",
    "\n",
    "        # Following car update without Kalman Filter\n",
    "        dx = z - pos2\n",
    "        err = (dx - dist_sep) / dist_sep\n",
    "        if err > 1.0: err = 1.0\n",
    "        # Acceleration model\n",
    "        if err > 0:\n",
    "            acc2 = err * car2_maxacc\n",
    "        else:\n",
    "            acc2 = err * car2_maxbrake\n",
    "        vel2 = vel2 + acc2 * dt\n",
    "        # crash protection \n",
    "        if vel2 < 0:\n",
    "            vel2 = 0\n",
    "            acc2 = 0\n",
    "        # update position\n",
    "        pos2 = pos2 + vel2 * dt + 0.5 * acc2 * dt**2\n",
    "\n",
    "        # Following car update with Kalman Filter\n",
    "        dx_kf = f.x[0, 0] - pos2_kf\n",
    "        err_kf = (dx_kf - dist_sep) / dist_sep\n",
    "        if err_kf > 1.0: err_kf = 1.0\n",
    "        # Acceleration model\n",
    "        if err_kf > 0:\n",
    "            acc2_kf = err_kf * car2_maxacc\n",
    "        else:\n",
    "            acc2_kf = err_kf * car2_maxbrake\n",
    "        vel2_kf = vel2_kf + acc2_kf * dt\n",
    "        # crash protection\n",
    "        if vel2_kf < 0:\n",
    "            vel2_kf = 0\n",
    "            acc2_kf = 0\n",
    "        # update position\n",
    "        pos2_kf = pos2_kf + vel2_kf * dt + 0.5 * acc2_kf * dt**2\n",
    "\n",
    "        # Update arrays for plotting\n",
    "        global timepoints, poscar1, poscar2, poscar1_est, car_sep\n",
    "        timepoints[tt] = tt * dt\n",
    "        poscar1[tt] = pos1real\n",
    "        poscar1_est[tt] = f.x[0, 0]\n",
    "        poscar1_meas[tt] = z\n",
    "        poscar2[tt] = pos2\n",
    "        poscar2_kf[tt] = pos2_kf\n",
    "        car_sep[tt] = pos1real - pos2\n",
    "        car_sep_kf[tt] = pos1real - pos2_kf  \n",
    "        car2_acc[tt] = acc2\n",
    "        car2_acc_kf[tt] = acc2_kf\n",
    "\n",
    "        # Update plots\n",
    "        #first row\n",
    "        r10.data_source.data['x'] = [pos1real, pos2, pos2_kf]\n",
    "        push_notebook(handle=target2)\n",
    "        #second row\n",
    "        r1.data_source.data['x'] = timepoints\n",
    "        r1.data_source.data['y'] = poscar1\n",
    "        r2.data_source.data['x'] = timepoints\n",
    "        r2.data_source.data['y'] = poscar1_meas\n",
    "        r3.data_source.data['x'] = timepoints\n",
    "        r3.data_source.data['y'] = poscar1_est\n",
    "        r4.data_source.data['x'] = timepoints\n",
    "        r4.data_source.data['y'] = poscar2\n",
    "        r5.data_source.data['x'] = timepoints\n",
    "        r5.data_source.data['y'] = poscar2_kf\n",
    "        r6.data_source.data['x'] = timepoints\n",
    "        r6.data_source.data['y'] = car_sep\n",
    "        r7.data_source.data['x'] = timepoints\n",
    "        r7.data_source.data['y'] = car_sep_kf\n",
    "        r8.data_source.data['x'] = timepoints\n",
    "        r8.data_source.data['y'] = car2_acc\n",
    "        r9.data_source.data['x'] = timepoints\n",
    "        r9.data_source.data['y'] = car2_acc_kf\n",
    "        push_notebook(handle=target1)\n",
    "\n",
    "        time.sleep(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the initial values in the cell below to simulate different conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial values\n",
    "car1_initpos = 3  # starting position of the car in the front\n",
    "car2_initpos = 0  # starting position of the following cars (keep at 0)\n",
    "car1_vel = 4  # velocity of the car in the front\n",
    "car2_initvel = 4  # starting velocity of the following cars\n",
    "car_size = 1  # size of the car \n",
    "dist_sep = 3 * car_size  # desired ideal separation between the car in the front and the cars following\n",
    "dist_size = 20  # length of the track in arb. units\n",
    "car1_acc = 0  # accelaration of the car in the front (keep at 0)\n",
    "car2_initacc = 0 # starting accelaration of the following cars\n",
    "car2_maxacc = 2  # maximum possible accelaration for the following cars\n",
    "car2_maxbrake = 40  # maximum possible decelaration for the following cars\n",
    "noise_type = 'gauss'  # type of measurement noise (can use 'gauss', 'power', and 'uniform', if anything else is entered measurement noise will be 0\n",
    "measurement_noise_level = 0.5  # use a number between 0.1 and 1.5\n",
    "process_noise_level = 0.5  # use a number between 0.1 and 1.5\n",
    "dt = 0.015  # time step (keep at 0.015)\n",
    "timesteps = int((dist_size - car1_initpos) / car1_vel / dt)  # total number of timesteps "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
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       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
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       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
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estimated position\"},\"renderers\":[{\"id\":\"ff9e8b05-a3ec-4631-adea-08cce1bded14\",\"type\":\"GlyphRenderer\"}]},\"id\":\"dc79d750-c03a-41c1-9c38-b472d9561a09\",\"type\":\"LegendItem\"}],\"root_ids\":[\"2adeaf5b-04e4-44db-b73a-18c2811294d9\"]},\"title\":\"Bokeh Application\",\"version\":\"0.13.0\"}};\n",
       "  var render_items = [{\"docid\":\"f39ef55f-ce9c-44fe-8c3d-e60901496e21\",\"notebook_comms_target\":\"2ce662b8-cd50-4c01-8cb1-399a8325e9c2\",\"roots\":{\"2adeaf5b-04e4-44db-b73a-18c2811294d9\":\"f7cebbd0-c1a2-437e-964b-2dbc839107d8\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2adeaf5b-04e4-44db-b73a-18c2811294d9"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target3 = show(row(p5, p6), notebook_handle=True)\n",
    "target2 = show(p4, notebook_handle=True)\n",
    "target1 = show(row(p1, column(p2, p3)), notebook_handle=True)\n",
    "RunCarFollowing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}