Notes
Quick Thoughts & Ideas
Short-form content, quick insights, and bite-sized learnings from my daily experiences in tech and development.
My note for lesson 6 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: Identify several different approaches Apollo uses to develop trajectories for autonomous vehicles. In planning, we incorporate HP maps, localization and prediction to build a trajectory for the vehicle. The first step is route ...
My note for lesson 5 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: Study different ways to predict how other vehicles or pedestrians might interact with Apollo self-driving cars.. After perceiving the world using sensors, we need to predict how the world is going to look in the future. It's im...
My note for lesson 4 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: Identify different perception tasks such as classification, detection, segmentation. ## Intro Perception module is much like our brain. It receives data from car sensors such as cameras, LiDARs, radars and use AI models and alg...
This is my note for lesson 3 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: How the vehicle localizes itself with a single-digit-centimeter-level accuracy. ## Localization methods in Apollo - The RTK (Real Time Kinematic) based method which incorporates GPS and IMU (Inertial Measurement Unit) i...
This is my note for lesson 2 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: High Definition maps for self driving cars. HD Maps have a high precision and contain a lot of information than your ordinary map on smartphone, such as lane line markings, 3D representation of the road network, traffic...
| | High traffic accident rate | More reliable driving | | Learn to drive from scratch | Learnable driving system | | Parking trouble | No parking trouble | ## Six levels of self-driving car - Level 0: Base level - No autonomous task - Level 1: Driver assistance - Driver Fully Engaged - Level 2...
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Classification is a basic family of models in machine learning. In very naive logic, people can use accuracy to evaluate how good a model is. However, do we really want accuracy as a metric for our performance? Actually, there are many metrics to evaluate a classification model depending on our problem in a real situa...