Deutsch
Français
Español
Italiano
Português
中文
日本語
Русский
Türkçe
WAYMO YOUR OWN “DAILY DRIVER”
Duration: 0:12
Views: 4
Submitted: 3 days ago
Submitted by:
Description:
WAYMO?
Damn, had engineered my first autonomous vehicle in 2921 and had my car drive me around usually 309 miles… Made a video and posted on FB, but WAYMO — my thoughts?
How bout I reveal to you how to automate your clunker as well?
Creating a self-driving car like Waymo’s involves cutting-edge hardware as you can see in my capture — sophisticated software, and massive computational resources… But you are in luck cause ai’ll break it down for you..
First, let’s take a deep dive into Waymo’s technology stack, followed by my simplified explanation (and my code) for building a basic autonomous system. Buckle up—this is a long ride.
Let’s START WITH Waymo’s Hardware Components?
Waymo’s self-driving system (the “Waymo Driver”) integrates custom-built hardware:
LiDAR (Light Detection and Ranging) and I got them on everything even my freaqin iPhones and use them all the time for CLASSIFIED SHIT WINK WINK!
It got Custom 360° LiDAR, Waymo’s proprietary called “Laser Bear Honeycomb” LiDAR uses multiple wavelengths to detect objects up to “300 meters away and It generates a 3D point cloud (millions of data points per second) to map surroundings in real time. It also has Short-Range LiDAR for low-speed, close-range detection (e.g., pedestrians, cyclists) and Intested it by standing right next to it then walking behind it flingin a middle finger! It worked but their vehicles also got a “Radar” which is an “Imaging Radar” capable of high-resolution tracking /speed and trajectory of objects, even in fog or rain as it operates at “76–81 GHz” for millimeter precision. And as far as many Cameras they appear to be “High-Resolution Cameras” judging from the lens quality — with HDR (High Dynamic Range) capability for color, texture, and traffic-light detection. I also observed “Thermal Cameras” which are great for night vision and detecting heat signatures (e.g., animals). But this gizmo also has “Ultrasonic Sensors” apparently which are short-range proximity detection for parking and low-speed maneuvers. It also has “Inertial Measurement Unit (IMU)” which is a must on these suckers as it tracks acceleration, angular rate, and orientation for dead reckoning (e.g., what I mean is tunnels and or garages (though I doubt this shit goes below ground unless it is done for the day in which case AGPS might be included?) with no GPS). And as far as their “Computer Platform?” Definitely Custom “AI accelerators” and “GPU clusters” for real-time processing most likely capable of ~30+ TOPs (Tera Operations Per Second) of computing power.
And What Anout Waymo’s Software Stack because the software converts sensor data into driving decisions:
1.Perception?
- Object Detection? Machine learning models (CNNs, Transformers) identify cars, pedestrians, traffic lights, etc.
- Sample Tools? TensorFlow, PyTorch, Waymo’s custom frameworks probably…
- Tracking & Prediction? Algorithms predict trajectories of objects (e.g., “Will that panhandler who just finger a finger and walked up step into the road again?”).
- Sensor Fusion? Combines LiDAR, radar, and camera data into a unified "world model" using “Kalman Filters” or “Bayesian networks.”
What About Localization & Mapping?
- Definitely HD Maps which are “Pre-built 3D maps” (cm-level accuracy) with traffic rules, lane markings, and landmarks.
- SLAM (Simultaneous Localization and Mapping) Real-time localization using LiDAR scans matched against HD maps.
Planning?
Definitely Behavioral Planning” which decides lane changes, turns, and responses to traffic. Uses “reinforcement learniing” and rule-based systems.
- Trajectory Generation? Creates a smooth, collision-free path using “spline curves” or “optimization algorithms” (e.g., Model Predictive Control).
Control?
PID Controllers or “MPC (Model Predictive Control)” which translates trajectories into steering, throttle, and brake commands. Very easy to achieve!
Simulation & Validation?
Carcraft most likely which is Waymo’s virtual world with billions of simulated miles. Tests edge cases (e.g., rogue shopping carts, aggressive drivers).
- And Fuzz Testing? Randomizes parameters (weather, sensor noise) to stress-test the system.
So we dissected this lamer crap in a nano, now a free tip on;
How to Build a Basic Self-Driving Car (Simplified)
For hobbyists or researchers, here’s my minimal workflow:
Your Sensor Setup
- Your Hardware
- LiDAR (e.g., Velodyne VLP-16)
- Cameras (e.g., Use Raspberry Pi + OpenCV)
- GPS/IMU (e.g., U-blox NEO-M8N)
- YOUR Software: ROS (Robot Operating System) for sensor data fusion.
2. Perception Code (MY Python Example)
```python
# Lane Detection with OpenCV
import cv2
import numpy as np
def detect_lanes(frame):
# Convert to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Edge detection
edges = cv2.Canny(blurred, 50, 150)
# Hough Transform to detect lines
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, maxLineGap=50)
return lines
# Main loop (reads from camera)
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
lanes = detect_lanes(frame)
# Draw lanes on frame
if lanes is not None:
for line in lanes:
x1, y1, x2, y2 = line[0]
cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 3)
cv2.imshow("Lane Detection", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
```
3. Next My Decision-Making (Rule-Based Example)
```python
# Basic decision logic (e.g., follow lane center)
def decide_steering_angle(lanes):
if len(lanes) >= 2:
left_lane = lanes[0]
right_lane = lanes[1]
# Calculate center between two lanes
center = (left_lane[0] + right_lane[0]) // 2
return center
else:
return "No lane detected—stop!"
```
4. Control (My Arduino Example)
```cpp
// Steering control via PID (Arduino)
#include <PID_v1.h>
double Setpoint, Input, Output;
PID myPID(&Input, &Output, &Setpoint, 2, 5, 1, DIRECT);
void setup() {
Input = read_sensor(); // Get current position
Setpoint = 0; // Target position (lane center)
myPID.SetMode(AUTOMATIC);
}
void loop() {
Input = read_sensor();
myPID.Compute();
steer(Output); // Adjust steering motor
}
```
Challenges I Encountered in 2021 Project I
1. High Sensor Cost (I won’t reveal how I solved this issue cheap); A single LiDAR can cost $75k+ (Waymo’s is custom and cheaper).
2. Compute Power? Real-time perception requires GPUs (Nvidia Jetson AGX I recommend to cheaply solve this issue for hobbyists).
3. Safety? Edge cases (e.g., brats or dogs chasing balls) require billions of training miles so don’t sit in the back like I did for 300 miles until you get your engineering down pact..
4. Local and State Regulation; COPS WILL ARREST YOUNIF
THEY CATCH YOU IN BACK SIT WHILE YOUR CAR IS DRIVING YOU content the fuckin windows LIMO TINT FIRST!
Highly illegal to do in every State but worth a mention that for bout $2,500 per vehicle ALL “ADAS-EQUIPPED” VEHICLES
IN USA CAN BE FULLY AUTONOMATED 100! You don’t need all this WAYMO SHIT and AI would still detect all humans and animals and be just FINE! Any chicle that has ADAS can be hacked/ retrofitted to be fully autonomous easily!
So WHY did I reveal this?
To show that entire USA can easily be FULLY AUTOMATED — not just manufacturing and Farming, but even stupid cars on the Highways!
Why is that important?
“Because if you don’t EVOLVE you DISOLVE!”
So why is USA not evolving?
Because you have to “Catch The Vision” first, wink — wink!
So how would this work
You stead of DOGE create “DOA” (Department Of Automation) so even Police. Ars are full autonomy capable! So say a cop has to get out to chase a suspect, on his watch he tells the car to come get him or; the car chases the suspect or the car whatever scenario, yeah it’s doable you just dumb as a doorknob! Anyways; after you automate every industry and every sector with DRONES deployed to autonomously do farming, all manufacturing, too to bottom chef to cook, then you revamp entire education system cause now you need to EDUCATE hence “PREP” next generation of “humans” for update you rolled out and wham, bam, Shazam! AI. An even solve hokicides way better than any human detective! By tracing DNA to nearest relative and that’s just for starters! You people need to smoke less crap and then you can rethink possible!
Easy easy!
And then get in your car’s BACK Seat, and you tell it; “take me home baby!”
China?
You can reengineer entire USA to put any human manufacturing dependent nation out of business! What you need to do is AUTOMATE! Auto MATE, dig?
*I AM NOT RESPONSIBLE IF YOU CRASH your vehicle while attempting to fully automate it, this post for entertainment only!
Damn, had engineered my first autonomous vehicle in 2921 and had my car drive me around usually 309 miles… Made a video and posted on FB, but WAYMO — my thoughts?
How bout I reveal to you how to automate your clunker as well?
Creating a self-driving car like Waymo’s involves cutting-edge hardware as you can see in my capture — sophisticated software, and massive computational resources… But you are in luck cause ai’ll break it down for you..
First, let’s take a deep dive into Waymo’s technology stack, followed by my simplified explanation (and my code) for building a basic autonomous system. Buckle up—this is a long ride.
Let’s START WITH Waymo’s Hardware Components?
Waymo’s self-driving system (the “Waymo Driver”) integrates custom-built hardware:
LiDAR (Light Detection and Ranging) and I got them on everything even my freaqin iPhones and use them all the time for CLASSIFIED SHIT WINK WINK!
It got Custom 360° LiDAR, Waymo’s proprietary called “Laser Bear Honeycomb” LiDAR uses multiple wavelengths to detect objects up to “300 meters away and It generates a 3D point cloud (millions of data points per second) to map surroundings in real time. It also has Short-Range LiDAR for low-speed, close-range detection (e.g., pedestrians, cyclists) and Intested it by standing right next to it then walking behind it flingin a middle finger! It worked but their vehicles also got a “Radar” which is an “Imaging Radar” capable of high-resolution tracking /speed and trajectory of objects, even in fog or rain as it operates at “76–81 GHz” for millimeter precision. And as far as many Cameras they appear to be “High-Resolution Cameras” judging from the lens quality — with HDR (High Dynamic Range) capability for color, texture, and traffic-light detection. I also observed “Thermal Cameras” which are great for night vision and detecting heat signatures (e.g., animals). But this gizmo also has “Ultrasonic Sensors” apparently which are short-range proximity detection for parking and low-speed maneuvers. It also has “Inertial Measurement Unit (IMU)” which is a must on these suckers as it tracks acceleration, angular rate, and orientation for dead reckoning (e.g., what I mean is tunnels and or garages (though I doubt this shit goes below ground unless it is done for the day in which case AGPS might be included?) with no GPS). And as far as their “Computer Platform?” Definitely Custom “AI accelerators” and “GPU clusters” for real-time processing most likely capable of ~30+ TOPs (Tera Operations Per Second) of computing power.
And What Anout Waymo’s Software Stack because the software converts sensor data into driving decisions:
1.Perception?
- Object Detection? Machine learning models (CNNs, Transformers) identify cars, pedestrians, traffic lights, etc.
- Sample Tools? TensorFlow, PyTorch, Waymo’s custom frameworks probably…
- Tracking & Prediction? Algorithms predict trajectories of objects (e.g., “Will that panhandler who just finger a finger and walked up step into the road again?”).
- Sensor Fusion? Combines LiDAR, radar, and camera data into a unified "world model" using “Kalman Filters” or “Bayesian networks.”
What About Localization & Mapping?
- Definitely HD Maps which are “Pre-built 3D maps” (cm-level accuracy) with traffic rules, lane markings, and landmarks.
- SLAM (Simultaneous Localization and Mapping) Real-time localization using LiDAR scans matched against HD maps.
Planning?
Definitely Behavioral Planning” which decides lane changes, turns, and responses to traffic. Uses “reinforcement learniing” and rule-based systems.
- Trajectory Generation? Creates a smooth, collision-free path using “spline curves” or “optimization algorithms” (e.g., Model Predictive Control).
Control?
PID Controllers or “MPC (Model Predictive Control)” which translates trajectories into steering, throttle, and brake commands. Very easy to achieve!
Simulation & Validation?
Carcraft most likely which is Waymo’s virtual world with billions of simulated miles. Tests edge cases (e.g., rogue shopping carts, aggressive drivers).
- And Fuzz Testing? Randomizes parameters (weather, sensor noise) to stress-test the system.
So we dissected this lamer crap in a nano, now a free tip on;
How to Build a Basic Self-Driving Car (Simplified)
For hobbyists or researchers, here’s my minimal workflow:
Your Sensor Setup
- Your Hardware
- LiDAR (e.g., Velodyne VLP-16)
- Cameras (e.g., Use Raspberry Pi + OpenCV)
- GPS/IMU (e.g., U-blox NEO-M8N)
- YOUR Software: ROS (Robot Operating System) for sensor data fusion.
2. Perception Code (MY Python Example)
```python
# Lane Detection with OpenCV
import cv2
import numpy as np
def detect_lanes(frame):
# Convert to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Edge detection
edges = cv2.Canny(blurred, 50, 150)
# Hough Transform to detect lines
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, maxLineGap=50)
return lines
# Main loop (reads from camera)
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
lanes = detect_lanes(frame)
# Draw lanes on frame
if lanes is not None:
for line in lanes:
x1, y1, x2, y2 = line[0]
cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 3)
cv2.imshow("Lane Detection", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
```
3. Next My Decision-Making (Rule-Based Example)
```python
# Basic decision logic (e.g., follow lane center)
def decide_steering_angle(lanes):
if len(lanes) >= 2:
left_lane = lanes[0]
right_lane = lanes[1]
# Calculate center between two lanes
center = (left_lane[0] + right_lane[0]) // 2
return center
else:
return "No lane detected—stop!"
```
4. Control (My Arduino Example)
```cpp
// Steering control via PID (Arduino)
#include <PID_v1.h>
double Setpoint, Input, Output;
PID myPID(&Input, &Output, &Setpoint, 2, 5, 1, DIRECT);
void setup() {
Input = read_sensor(); // Get current position
Setpoint = 0; // Target position (lane center)
myPID.SetMode(AUTOMATIC);
}
void loop() {
Input = read_sensor();
myPID.Compute();
steer(Output); // Adjust steering motor
}
```
Challenges I Encountered in 2021 Project I
1. High Sensor Cost (I won’t reveal how I solved this issue cheap); A single LiDAR can cost $75k+ (Waymo’s is custom and cheaper).
2. Compute Power? Real-time perception requires GPUs (Nvidia Jetson AGX I recommend to cheaply solve this issue for hobbyists).
3. Safety? Edge cases (e.g., brats or dogs chasing balls) require billions of training miles so don’t sit in the back like I did for 300 miles until you get your engineering down pact..
4. Local and State Regulation; COPS WILL ARREST YOUNIF
THEY CATCH YOU IN BACK SIT WHILE YOUR CAR IS DRIVING YOU content the fuckin windows LIMO TINT FIRST!
Highly illegal to do in every State but worth a mention that for bout $2,500 per vehicle ALL “ADAS-EQUIPPED” VEHICLES
IN USA CAN BE FULLY AUTONOMATED 100! You don’t need all this WAYMO SHIT and AI would still detect all humans and animals and be just FINE! Any chicle that has ADAS can be hacked/ retrofitted to be fully autonomous easily!
So WHY did I reveal this?
To show that entire USA can easily be FULLY AUTOMATED — not just manufacturing and Farming, but even stupid cars on the Highways!
Why is that important?
“Because if you don’t EVOLVE you DISOLVE!”
So why is USA not evolving?
Because you have to “Catch The Vision” first, wink — wink!
So how would this work
You stead of DOGE create “DOA” (Department Of Automation) so even Police. Ars are full autonomy capable! So say a cop has to get out to chase a suspect, on his watch he tells the car to come get him or; the car chases the suspect or the car whatever scenario, yeah it’s doable you just dumb as a doorknob! Anyways; after you automate every industry and every sector with DRONES deployed to autonomously do farming, all manufacturing, too to bottom chef to cook, then you revamp entire education system cause now you need to EDUCATE hence “PREP” next generation of “humans” for update you rolled out and wham, bam, Shazam! AI. An even solve hokicides way better than any human detective! By tracing DNA to nearest relative and that’s just for starters! You people need to smoke less crap and then you can rethink possible!
Easy easy!
And then get in your car’s BACK Seat, and you tell it; “take me home baby!”
China?
You can reengineer entire USA to put any human manufacturing dependent nation out of business! What you need to do is AUTOMATE! Auto MATE, dig?
*I AM NOT RESPONSIBLE IF YOU CRASH your vehicle while attempting to fully automate it, this post for entertainment only!
Categories:
People and Blogs
