Computer Modeling and Simulation of Autonomous Vehicles and Robots
Covers ROS, vehicle dynamics modeling, sensor simulation, terramechanics, and autonomy stacks for autonomous vehicles and robots.
Summary
Recent recorded grades — Spring 2022: 3.79 GPA, 88.5% A/AB (n=26 letter grades).
What you’ll be able to do
Use computer simulation in the process of designing and analyzing new autonomous vehicles and robots
Create a ROS (Robot Operating System) infrastructure for a simple robot or small scale autonomous vehicle
Create a ROS infrastructure used in conjunction with an autonomy stack to simulate the behavior of a robot or autonomous vehicle
Identify the main modeling techniques used to approximate through computer simulation the real world dynamics/behavior of autonomous vehicles and robots
Gain a panoramic image of modeling and simulation requirements associated with the multi-disciplinary task of designing autonomous vehicles and robots
Integrate the activities of designing, implementing, building, validating and positioning for release software for modeling and simulation of autonomous vehicles and robots
Propose and evaluate alternate or novel methods to achieve modeling and simulation objectives, defend opinions
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapM E 459 and (COMP SCI 200, 220, 300, 301, 320, or placement into COMP SCI 300), graduate/professional standing, or member of Engineering Guest Students
- ME 459
- COMPSCI 200
- COMPSCI 220
- COMPSCI 300
- 301
- COMPSCI 320
- placement into COMP SCI 300
- graduate/professional standing
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- All of
- ME 459
- Any of
- COMPSCI 200
- COMPSCI 220
- COMPSCI 300
- 301
- COMPSCI 320
- placement into COMP SCI 300
- graduate/professional standing
- member of Engineering Guest Students
- All of
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Recent recorded grades — Spring 2022: 3.79 GPA, 88.5% A/AB (n=26 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Robot Operating System (ROS)
- Vehicle dynamics modeling and simulation
- Sensor simulation (camera, lidar, radar, GPS, IMU)
- Terramechanics modeling
- Autonomy stack (sensing, perception, planning, control)
- Artificial intelligence in autonomy
- Verification and validation
Skills
- Computer simulation for designing and analyzing autonomous vehicles and robots
- Creating ROS infrastructure for robots and autonomous vehicles
- Vehicle dynamics modeling including tire, suspension, steering, and powertrain systems
- Sensor simulation for cameras, lidar, radar, GPS, and IMU
- Terramechanics modeling for mobility on deformable terrains
- Autonomy stack concepts including sensing, perception, planning, and control
Grades
Latest available · Spring 2022— not enough history to project Spring 2027.
Grade distribution · % of letter grades
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "ff95fd52c6f72f4ddad2d271ca25505d1d4334c647885db2af28858eec6b934a",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}