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Project courseThe student keeps the work and the record they made.
Stanford CARLO: Safe Intersection Experiments
Grades 9-12: crash risk at intersections, safety rules...
Where this course comes fromThe universities and labs named here made the open projects or materials this course draws on. CIT designed the course independently; it is not an official, affiliated, or endorsed course of those institutions.
- Stanford UniversityOpen project it is built on
- Free consultation and level check
- Apgujeong classroom or online
- One-to-one or small group
- Registered academy no. 11882
Is Stanford CARLO: Safe Intersection Experiments a good fit for high school students in Grades 9-12?
Stanford CARLO: Safe Intersection Experiments is a good fit for students in grades 9-12 who are interested in crash risk at intersections and safety rules for self-driving cars. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a self-driving safety simulation app, an intersection screen that replays risk timelines, and collision-check code with tests.
The course is built on an open project published by Stanford. Students place cars and pedestrians at an intersection in the open CARLO example and change their speeds to find when crash risk rises. They adjust a car's speed and direction, check for pedestrian collisions, and build a feature that edits vehicle and pedestrian rules and checks whether the car stops automatically just before a collision. An optional large language model (LLM) only turns a plain-language description into allowed speed, direction, and signal settings, which run in the simulation only after the student reviews them. The student then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
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- Age group
- Grades 9-12
- Academic subject
- AI & Data, Engineering & Robotics, Physics & SpaceBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Lesson-readyEvery learning item includes the evidence, worked example, practical work, and tutor guidance required by CIT's lesson-ready standard.
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. The material this course reads was made at Stanford University. Names identify the source, not affiliation or endorsement.
- Stanford UniversityOpen project it is built on
How can I explain this course to my child?
If questions about crash risk at intersections or safety rules for self-driving cars keep making you ask why, Stanford CARLO: Safe Intersection Experiments lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- crash risk at intersections
- safety rules for self-driving cars
- car and pedestrian speeds
What does the student finish?A self-driving safety simulation app, an intersection screen that replays risk timelines, and collision-check code with tests.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Course placement follows current subject and coding readiness.
Students should be ready to document sources, methods, and limits.
Advanced tools are introduced after a clear baseline.
What will my child learn?
- Explain how the positions, speeds, and directions of cars and pedestrians decide whether they collide.
- Change only the car's speed and compare when dangerous moments appear at the same intersection.
- Build a feature that finds near-collision moments and checks whether the automatic stop works.
- Design a screen that sets the intersection layout, speeds, and right of way and replays a risk timeline.
- Stanford UniversityOpen project it is built on
How does the course progress?
- 1Understanding the Research Question and Baseline Behavior
Run the first intersection from the open example and map where cars and pedestrians start and how the car moves.
- 2Connecting Sensors, Actions, and Measurements
Change the car's speed and direction and compare ways to check for pedestrian collisions from the same start.
- 3Building a Feature and Control Screen from Evidence
Vary only the car's speed to compare risk, then code a check that finds near-collision moments and tests the automatic stop.
- 4Testing the Integrated App and Explaining the Design
Connect an intersection safety screen for speed and right of way with the optional LLM, and finish with a three-minute demo.
What counts as useful evidence?
A self-driving safety simulation app, an intersection screen that replays risk timelines, and collision-check code with tests.
What should an admissions reader be able to see?
The course is built on an open project published by Stanford. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
These names identify who published the open project the course starts from. The evidence an application reads is what the student understood, completed, and can explain.
The real research project this course reads
- Stanford-ILIAD/CARLO
- pinned commit
1dc7ebe4ca1f - commit date 2022-02-05
- licence MIT
- text files 12
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
InputRoads and placing
- world.py
- entities.py
MemoryPositions, shapes
- entities.py
- geometry.py
ProcessMove, then overlap
- world.py
- geometry.py
OutputPicture, collision
- visualizer.py
ControlThe loop
- example_intersection.py
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.
On a narrow screen, swipe the picture sideways.
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.
- Stanford UniversityOpen project it is built on
Questions parents search before choosing this course
Is Stanford CARLO: Safe Intersection Experiments a good fit for high school students in Grades 9-12?
Stanford CARLO: Safe Intersection Experiments is a good fit for students in grades 9-12 who are interested in crash risk at intersections and safety rules for self-driving cars. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a self-driving safety simulation app, an intersection screen that replays risk timelines, and collision-check code with tests.
Can my child take Stanford CARLO: Safe Intersection Experiments online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for Stanford CARLO: Safe Intersection Experiments?
Course placement follows current subject and coding readiness. Students should be ready to document sources, methods, and limits. Advanced tools are introduced after a clear baseline.
What will my child make or practice in Stanford CARLO: Safe Intersection Experiments?
The main evidence is a self-driving safety simulation app, an intersection screen that replays risk timelines, and collision-check code with tests. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can Stanford CARLO: Safe Intersection Experiments show research understanding in a college application?
The course is built on an open project published by Stanford. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
How are the schedule and tuition for Stanford CARLO: Safe Intersection Experiments determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
I appreciated that a simple early idea was extended into an 'AI debate tool'. My child has always been interested in debate and social issues, so seeing that interest connect to the project makes me expect a more complete and distinctive result.
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Choose the course after a readiness check
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