- Haoru Xue - Electrical Engineering
- Yuhan Zhang - Electrical Engineering
- Cheyenne Herrera - Math/Engineering
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.
- 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)
- USB camera
- Arduino (for connecting ToF sensor)
- Time-of-flight sensor (ToF)
- Lidar and USB controller
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.
To use the PyLidar3 library
Create an instance and connect
lidar = YdLidarX4("/dev/ttyUSB0") lidar.Connect()
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
myArduino.flushInput() ser_bytes = myArduino.readline() try: 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
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)*50 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
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 regions of the Lidar scan to do this. The black region is blocked by the car itself. It is tested to be under 45 degrees on both sides. Similar to the last part we set a virtual "wall" on both sides of the car: we calculate the distance using sine and set a threshold 300 millimeters. If some object is on one side of our car, we block the ability to turn further in that direction. The measurement range is very large, larger than other cars. Also, sometimes only part of the object can be detected in the range. So we assume there is an object when at least 20% of the distances are under the threshold.
dis =  # turn right if angle - self.last_angle > 0: dis = self.distances(scan, True) # turn left if angle - self.last_angle < 0: dis = self.distances(scan, False) if angle== 0: self.last_angle = angle return angle disnp = np.array(dis) if sum(disnp<self.mindis)/len(disnp) > self.threshold: return self.last_angle # locked else: self.last_angle = angle return angle
This rule doesn't work well when there is an object on one side and the road itself is turning to that direction. Therefore the action of this system is better to be a warning rather than locking the steering. Another way would be using lane change detection rather than steering angle increment as the condition to activate the detection.
Front collision prevention
The front collision prevention is similar to the rear one. The Lidar is blocked by the car itself so we cannot get distance measurements in front of the car. Instead a ToF sensor is installed to do that job.
Protential side collision prevention
In this task, we use a combination of techniques in rear collision prevention and safe lane change. For side collision prevention we don't need that many measurements as in safe lane change. Because for lane changing we need to make sure the car behind in the other lane is far enough such that they can be aware of our lane changing. But for side collision prevention we just want to keep objects outside our virtual "walls" and minimize false alarms. The wall behind is 200 millimeters and the walls on the sides are 300 millimeters from the Lidar. So the angles for more than 90 degrees should be arctan(200/300) which is about 34 degrees.
After finding the region to look at, we use the technique we used in the safe lane changing task, to detect an object when at least 25% of the distances are under the threshold. Then the average of distances under the threshold is used for calculating the speed using the technique in the rear collision prevention task.
When there is an obstacle in the front and there is a car approaching fast from behind, our car try to change lane to avoid the collision.
- Donkey Parts
- How Donkey works
- 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
Over the course of the semester, we faced many challenges. One of the biggest challenges that we faced during this project was adding the LiDAR and TOF sensor to the donkey framework. This is due to the fact that the donkey library is very long and complex; however, we were able to accomplish this after getting help from TA's/outside resources. Likewise, there were some challenges that we could unfortunately not figure out. This includes the latency for the LiDAR to transfer data to the jetson via serial. What this means is that when we were testing the rear end collision, there was a significant lag time between unsafe approach and the response of the car. The sampling was around 8Hz and the data transfer was around 2Hz. Furthermore, our lane changing method lacked the fine tuning it needed. We had pushed off training, and in the final days of our project, the weather was rainy and we were not able to receive the results we wanted.