First understand what Stanford researchers are trying to learn through ILIAD, why the question matters, how they test it, and what the result cannot prove. Change car and pedestrian speeds in an on-screen intersection and see when close calls become more frequent. The student then completes An autonomous-driving safety simulator for creating intersection rules and testing near-collisions and automatic stopping. For a college application or interview, the student separates the source research from their feature, interface, tests, failures, and revisions. Available online or in person in Apgujeong, in one-to-one or small-group formats.
University and lab names show where each public project came from. CIT independently designed these courses; they are not official university courses, partnerships, or endorsements.
8guided sessions
2-4 hoursto the first small project
Beginnerrecommended level
Not requiredphysical robot
Primary routes
Choose the student's primary AI education and portfolio route
CIT keeps the international-school route first, followed by the separate science and gifted-school route.
CIT lessons can run online across Korea and overseas or in person in Apgujeong. The student owns the question, code, tests and explanation; classes do not guarantee admission, selection or awards, and online delivery never changes an institution's submission rules.
When does crash risk rise as car and pedestrian speeds change at an intersection?
Move cars and pedestrians through a simple on-screen intersection. Change speed and the rule that chooses direction to see when crash risk rises.
Students first use a small working example to understand the researchers' question, method, and evidence. A controlled change helps identify what the student's extension must solve. The student then implements a useful feature, connects an operator interface, and tests the integrated application in normal, boundary, and failure cases.
What will the student complete?
An autonomous-driving safety simulator for creating intersection rules and testing near-collisions and automatic stopping
The final package includes runnable instructions, the feature and interface design, normal and failure tests, one documented revision, and a three-minute explanation in the student's own words.
Feature
A feature for editing vehicle and pedestrian rules, finding near-collision scenes, and checking whether the car stops
Operator interface
An intersection-safety screen for choosing layout, speed, and right-of-way and replaying a risk timeline
Optional LLM boundary
Optional: an LLM converts a natural-language scenario into an allowed speed, direction, and signal draft. It runs only in simulation after student review.
Integrated result
An autonomous-driving safety simulator for creating intersection rules and testing near-collisions and automatic stopping
Four ideas explained in this course
01car and pedestrian positions
02how a car moves
03checking for collisions
04rules that choose direction
Eight introductory sessions
Understand the research, then build and test a working robotics application
The 20 courses are eight-session CIT studios in which students understand a public university or lab project, confirm a working example, and then turn it into a small robotics application with a useful feature and an operator interface. Selected courses add an optional LLM explanation tool that can read run records but cannot control the robot. RoboMaster has 28 sessions, and implementation scope is adjusted to each student's experience and computer access.
Understand the source
01
Run the first public intersection example
02
Understand cars, pedestrians, and starting positions
Measure and compare
03
Change car speed and direction
04
Check for pedestrian collisions
Build a feature
05
Change only the car speed
06
Implement the student-owned feature: A feature for editing vehicle and pedestrian rules, finding near-collision scenes, and checking whether the car stops
Integrate and demonstrate
07
Build the operator interface: An intersection-safety screen for choosing layout, speed, and right-of-way and replaying a risk timeline. Add the bounded assistant: Optional: an LLM converts a natural-language scenario into an allowed speed, direction, and signal draft. It runs only in simulation after student review.
08
Integrate, test, and demonstrate: An autonomous-driving safety simulator for creating intersection rules and testing near-collisions and automatic stopping
Public project used in class
See the exact version CIT reviewed
Public projects can change over time. To keep the class example consistent, CIT uses version 1dc7ebe4ca1f of Stanford-ILIAD/CARLO. CIT checked it on 2026-08-14; it was created on 2022-02-05. Usage-rights note: MIT.
The student changes only the pedestrian's speed in a junction scene and finds where the risk suddenly jumps. In class the project is separated into five boxes (input, memory, process, output, control). The lesson opens one of them, changes one value, and leaves the rest closed.
InputWhat comes in?
The layout of roads and buildings, and where the car and the pedestrian start
MemoryWhat persists?
For each object, its position, speed, heading, and the shape that stands for its body
Processopened hereWhat transforms?
Moving everything forward by a short time, then deciding a collision by whether the shapes overlap
OutputWhat leaves, and who uses it?
The top-down picture on screen, and whether a collision happened
ControlWhat decides when anything runs?
The loop that repeats the move and the check at a fixed time step
The files the lesson opens, by name. The course is not a walk through the repository; it opens a chosen few and says which.
One value moves and everything else stays. The right-hand column is the prediction written before the run, not a result; the work is reconciling the two.
The universities and labs named here made the open projects this course reads. CIT designed the course independently; it is not an official, affiliated, or endorsed course.
Clear answers about what students do, what they need, and where the project came from.
Is Stanford CARLO: Safe Intersection Experiments an official course from Stanford ILIAD?
University and lab names show where each public project came from. CIT independently designed these courses; they are not official university courses, partnerships, or endorsements.
Is hardware required?
Not required. The course starts with a robot on the computer or a saved recording of a completed run. Computer guidance: Low-spec computer suitable.
What background should a student have?
Recommended level: Beginner. Students should be able to follow a guided Python example, test one controlled change, and then build and explain a small feature and interface.
What will the student make?
The completed project is An autonomous-driving safety simulator for creating intersection rules and testing near-collisions and automatic stopping. The student implements A feature for editing vehicle and pedestrian rules, finding near-collision scenes, and checking whether the car stops and An intersection-safety screen for choosing layout, speed, and right-of-way and replaying a risk timeline. Optional: an LLM converts a natural-language scenario into an allowed speed, direction, and signal draft. It runs only in simulation after student review.
How can the student use this project in a college application?
Explain the source research question, method, evidence, and limits first. Then separate the student's own feature and interface decisions, normal and failure tests, revisions, and next question. The source institution's name does not imply affiliation or guarantee admission.
Which project version does the course use?
CIT reviewed version 1dc7ebe4ca1f of Stanford-ILIAD/CARLO on 2026-08-14. That version was created on 2022-02-05. We keep this version during class so the example does not change unexpectedly, and we check the setup again before teaching.
Can Stanford CARLO: Safe Intersection Experiments be taken online or in person, one-to-one or in a small group?
Yes. This course is offered online and in person at CIT in Apgujeong, Gangnam-gu, Seoul, with one-to-one and small-group options. Placement and current availability are confirmed after a readiness consultation.
Is this a good first project for this student?
Before placement, we check the student's coding and math experience, available computer, interests, and ability to explain what happened.
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