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

  • Harou Xue - Electrical Engineering
  • Yuhan Zhang - Electrical Engineering
  • Cheyenne Herrera - Math/Engineering

Project Objectives

The goal of our project is to create a miniature version of a Tesla. We wanted to increase the safety of the self-driving car by implementing rear-end collision prevention as well as apply the lane change safety. The Donkey RoboCar will stop itself when approaching an object in the front using a TOF sensor mounted to it. Additionally, the car will speed up if a vehicle/object is approaching it from behind. Furthermore, the RoboCar will implement lane change on command.

Mechanical Design


Camera Mount.png

Adjustable Camera Holder.png

Electronic Design


The Lidar is mounted at the back of the car
  • Jetson Nano with fan and wirless card installed
  • PCA9685 PWM (control servo and ESC)
  • Steering Servo (control steering)
  • Electronic speed controller (ESC) (control throttle)
  • Relay (provide emergency stop)
  • LED (show emergency stop status)
  • power
  • USB camera
  • Arduino (for connecting ToF sensor)
  • Time-of-flight sensor (ToF)
  • Lidar and USB controller


Electronic Schematic.png


Donkey and parts

We use the Donkey Car framework for car control. With the framework, we can easily train deep learning autonomous driving models by recording manual driving. The frameworks use modularized "parts" to manage all the components in a car. When the car runs, it loops through all parts that have been added to it. Not only sensors can be Donkey parts, controllers and actuators are also Donkey parts.

Our project involves new sensors, ToF and Lidar, that have not been included in Donkey. They should be added to Donkey in the form of parts.

We connect the Lidar and ToF(Arduino) using serial ports. We need to add our user to a group that has access to serial ports.

   sudo usermod -a -G dialout jetson

We are using YD Lidar X4. There is a python library PyLidar3 that supports this model.

To use the PyLidar3 library

Create an instance and connect

   lidar = YdLidarX4("/dev/ttyUSB0")


   scans = self.lidar.StartScanning()
   for scan in scans:
       for i in range(360):
           self.scan[i] = scan[i]

Stop Lidar and disconnect


We use Adruino to connect to the ToF sensor.

   myArduino = serial.Serial('/dev/ttyACM0', 115200)

Get sensor reads

   ser_bytes = myArduino.readline()
       distance = float(ser_bytes[0:len(ser_bytes) - 2].decode("utf-8"))

Donkey uses a dictionary to save data that flow through each part. The data used as input or output of parts should be defined when adding parts.

Example: adding Lidar to Vehicle in manage.py

   V.add(lidar, outputs=['lidar/scan', 'lidar/time'], threaded=True)

Lane changing behavior training

Donkey framework supports training different behavior states. We can use that to train a model that is able to perform lane changing. https://docs.donkeycar.com/guide/train_autopilot/#training-behavior-models

Rear collision prevention

Yellow regions are used for safe lane changing and the Blue region is for rear collision prevention

Our first goal is to provide rear collision prevention. When another car is going to crash into the back of our car and there is no obstacle in front of our car. We can accelerate forward to prevent a collision. To use Lidar to detect objects in the back. The car is 250 millimeters in width so if we want to detect something that wide 200 millimeters away from the back, we need at least about 60 degrees of Lidar measurement.

We set a detection range of 60 degrees, 150 to 210, and from 200 millimeters to 2000 millimeters. We use cosine to calculate the real distance from lidar readings which include angles and distances. Then the median of all 60 distances is selected to be the distance between our car and the object in the back.

First, we just set a threshold, when the distance is lower than 200 millimeters we add throttle by 0.1. It works as intended preventing rear collision but there are some lags and it cannot go faster if the object in the back does not stop.

To mitigate the problem above, we look further and act earlier. Relative speed is calculated using two adjacent distance measure and time between. If the speed of the car is lower than the speed of back object in the same direction, we will add a value proportional to relative speed and inverse proportional to distance. If the distance goes inside 200 millimeters the car will run at its full speed.

Here is part of the code that implements the system described above.

   # when lidar is not updated, keep last change
   if time == self.lasttime:
       return self.gett(throttle)
   distance = self.get_d(scan)
   if distance == 0 or distance > self.maxdis:  # out of range is often 0
       self.lasttime = time
       self.lastdis = distance
       return self.gett(throttle)
   if distance <= self.mindis
       self.lasttime = time
       self.lastdis = distance
       return 1
   if self.lastdis != 0:
       rspeed = (distance - self.lastdis) / (time - self.lasttime)
       if rspeed >= 0:
           self.lasttime = time
           self.lastdis = distance
           return self.gett(throttle)
       self.throttle_change = rspeed/(self.mindis - distance)
       self.lasttime = time
       self.lastdis = distance
       return self.gett(throttle)

gett() here is adding the change to throttle and capped by 1.

Safe lane changing

The red car cannot be seen in mirrors

When changing lane, it is not safe to just look at mirrors. Using Lidar we can detect cars in the blind spot and override steering control to prevent unsafe lane changing.

More specifically, use the yellow part of the Lidar scan to do this

Front collision prevention

Protential side collision prevention

Smart escape

Useful Knowledge

Donkey Parts


How Donkey works




Future Work