From MAE/ECE 148 - Introduction to Autonomous Vehicles
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Team Members

  • Han Zhao(ECE)
  • Zhetai Zhou(ECE)
  • Felix Koch(MAE)

Project Overview

The objective is to measure another car's speed using a second stationary jetson nano with two webcams plugged into it. Those two cameras will be set up at a given distance (e.g. 3m) and be trained using AI to give a signal when detecting a car passing. With this data and the time we can calculate the velocity and compare it to our speed limit. If the car turns out to be speeding our parked police car will turn on some LED lights and join the track to chase the traffic law violator.


Must Haves

  • Two webcams
  • Two jetson nanos(One can be replaced by a virtual machine)
  • Another RC car for speed trap

Nice to Haves

  • Two lidars which can be used to replace two webcams to measure the speed

Project Video

Project Presentation


Mechanical Design

The major components of the mechanical design include the baseplate, camera mount, and Jetson Nano case.


Camera Mount

Jetson Nano Case

Electrical Design



Code For Speed Trap

# OpenCV Python program to detect cars in video frame # import libraries of python OpenCV import socket import cv2 import time # capture frames from a video first = False second = False start = 0 end = 0 #First Camera cap1 = cv2.VideoCapture(0) #Second Camera cap2 = cv2.VideoCapture(1) #threshold for first Camera threshold1 = 7 #Threshold for second Camera threshold2 = 2 #Distance between two cameras distance = 2.2 idle = 20 v = 0 speed = False current = 0 stop = 0 sleep_time = 5 #Speed Limit speed_limit = 2 #Background for first camera background1 = None #Background for second camera background2 = None scale1 = None scale2 = None #create a server s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind(("",23878)) s.listen(5) data = "0" old_data ="0" # loop runs if capturing has been initialized. while True: #check connection clientsocket, address = s.accept() print(f"Connection from {address} has been establish.") while True: # reads frames from a video ret1, frames1 = cap1.read() ret2,frames2 = cap2.read() # convert to gray scale of each frames gray1 = cv2.cvtColor(frames1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(frames2, cv2.COLOR_BGR2GRAY) if background1 is None: background1 = gray1 if background2 is None: background2 = gray2 substraction1 = cv2.absdiff(background1, gray1) substraction2 = cv2.absdiff(background2,gray2) sum1 = cv2.sumElems(substraction1) sum2 = cv2.sumElems(substraction2) if scale1 is None or scale1 == 0: scale1 = sum1[0] if scale2 is None or scale2 == 0: scale2 = sum2[0] if scale1 != 0: #if the change is greater than the threshould, set the change be true if(sum1[0]/scale1 > threshold1 and not first): first = True #time starts start = time.time() print(sum1[0] /scale1) print("Car passing in first") if scale2 != 0: if(sum2[0]/scale2 > threshold2 and not second and first): second = True #time ends end = time.time() print(sum2[0] /scale2) print("Car passing in second") current = time.time() #print the speed of the car if(first and second and not speed): print("speed:") v = round(distance/(end-start),2) print(str(v) + "m/s") speed = True stop = time.time() if(stop != 0 and (current - stop) > sleep_time): first = False second = False stop = 0 speed = False #send the signal to client if(old_data != data): clientsocket.send(bytes(data,"utf-8")) old_data = data if(v > speed_limit): data = "1" time.sleep(0.05) cv2.waitKey(1) cv2.destroyAllWindows()

Code For Robocar


Donkey Car Deep Learning Autonomous Laps

ROS Autonomous Laps

ESP32 with E-Stop