SlipX

SlipX: vehicle dynamics for 1/10-scale racecars

Vehicle dynamics for 1/10-scale autonomous racecars, the RoboRacer and F1TENTH class. C++17, CPU-only, headless, Apache-2.0.

Two things separate it from the physics in a game engine. It is a library before it is a simulator: slipx_core depends on the C++ standard library and nothing else, so it goes inside a stack you already run. And every tyre parameter is identifiable from a manoeuvre driven in a car park with the sensors already on a competition car, so there is nothing to guess.

Early days. Three of the four tiers are built, and a car directory parameterises all three: schema 0.2.0 carries the longitudinal slip stiffness, the ESC curve, the battery endpoints and the servo constants the double-track tier consumes. The extended tier raises rather than handing back a simpler model. No parameter set here has been fitted to a real vehicle, and the tooling says so every time it loads one.

Quickstart

pip install slipx
slipx-conformance

Wheels cover Linux, macOS and Windows on CPython 3.9 to 3.13, so the normal case needs no compiler. slipx-conformance integrates the canonical step steer and prints the run manifest and its trajectory hash. 0.2.0 is the first final release; 0.1.0a1 before it was a pre-release spent proving the packaging path, there is no 0.1.0, and until 1.0.0 a minor bump may break the API.

import slipx

car = slipx.load_reference_car()   # or load_car("examples/cars/reference_1_10")
print(car.summary())               # leads with the provenance label: PROVISIONAL

model = slipx.VehicleModel.create(slipx.Tier.L1_Bicycle, car.params)
state = slipx.VehicleState()
state.vel_body.x = 5.0

diagnostics = slipx.StepDiagnostics()
for _ in range(1000):
    model.step(state, slipx.DriveInput(steer_cmd=0.1), 1e-3, diagnostics)

print(state.yaw_rate, diagnostics.alpha_front, diagnostics.ay)

StepDiagnostics is optional so the hot path stays cheap: ask for it and you get slip angles, slip ratios, per-tyre forces, load transfer and saturation flags. Anything a tier cannot represent comes back as NaN, never zero, so a plot of the kinematic tier's slip angles is empty rather than flat at a number somebody would believe.

To look at a run, record it and write it out. MCAP is the default; Rerun and Foxglove read it.

sim = slipx.make_conformance_run(slipx.ConformanceSpec())
run = slipx.sinks.record_run(sim, duration=5.0, stride=10)
slipx.sinks.write(run, "lap")            # lap.mcap, needs slipx[mcap]

Tiers

Fidelity levels selected at construction, behind one VehicleModel interface, so moving between them is one argument rather than a rewrite.

Tier	Model	States	Status
`L0_Kinematic`	Kinematic bicycle	4	built
`L1_Bicycle`	Dynamic bicycle, linear tyres	6	built
`L2_DoubleTrack`	Double-track, load transfer, MF-lite tyres, relaxation, drivetrain	13	built
`L3_Extended`	Adds thermal and suspension		raises

L2_DoubleTrack has four contact patches, per-corner vertical loads, MF-lite with a real peak and falling branch, a combined-slip friction ellipse, a tyre relaxation transient, and the drivetrain: an open, spool or preloaded-LSD differential on a 2WD or 4WD layout, an ESC torque-speed curve with current and regen limits, battery sag and state of charge, and a slew-limited second-order steering servo, so commanded and achieved steer are separate numbers. Braking is motor braking through the driven axle, capped by the regen limit. It uses parallel steer rather than Ackermann, and it has no wheel rotational state, so a locked or spinning wheel is not representable. Each of those is named in the source.

Below L2_DoubleTrack nothing represents CoG height, weight distribution, differential or tyre compound, so those parameters correctly have no effect.

The tyre model

L1 uses a linear tyre, Fy = -C_alpha * alpha, clipped at mu * Fz. A clip has no peak and no falling branch, so it cannot spin a car; tyre_saturated fires the instant it engages.

L2 uses MF-lite, a reduced Magic Formula with load sensitivity and combined slip. Every parameter earns its place by being identifiable:

Symbol	Meaning	Identifiable from
`C_alpha0`	Cornering stiffness at nominal load	Skidpad, low-slip region
`C_kappa`	Longitudinal slip stiffness	Encoder slip ratio against IMU acceleration
`mu_y0`, `mu_x0`	Peak lateral / longitudinal friction	Circle-to-slip, straight-line accel
`k_mu`	Load sensitivity exponent	Skidpad at two ballast configurations
`C`, `E`	MF shape factors; `B` is derived, never asked for	Full slip sweep
`sigma`	Relaxation length	Step steer transient

Full Pacejka is not the goal. A parameter nobody can identify is worse than one that does not exist, because it gets guessed and then trusted.

Tyres are referenced as a (compound, surface) pair rather than embedded in the car file. The same car on carpet and on polished concrete is two different vehicles.

Determinism

Fixed step, seeded per agent, lockstep barrier, headless. Every run writes a manifest hashing the schema versions, parameter files, seeds, integrator, git SHA, compiler ID and flags.

On one build, the same manifest and inputs replay bit-identically. That needs -ffp-contract=off, no -ffast-math, no -march=native, a fixed reduction order and no multithreading inside the integrator.

Across builds, bit-identity is not promised. conformance/reference_hashes.tsv is keyed by architecture, compiler, C library and build type, and tools/check_conformance.py compares a run against the row matching its own build. A mismatch there is a bug; a build with no row is a build about which nothing was claimed.

So slipx-conformance printing a different hash on your machine is expected, and published wheels carry no hash claim at all. The variable is libm, not the compiler: one wheel hashes differently on glibc 2.28 and 2.39 while agreeing to every printed digit, and every x86-64 reference row is identical across GCC 11, GCC 13 and Clang 18. That is why the C library is in the key and why the manifest records it.

Building from source

cmake -S . -B build -DSLIPX_BUILD_PYTHON=ON
cmake --build build -j
ctest --test-dir build && python3 -m pytest

Needs CMake 3.20 and a C++17 compiler. GoogleTest is fetched if absent; the Python parts want PyYAML, jsonschema and pybind11. None of that reaches slipx_core, which builds and passes its full suite alone under -DSLIPX_CORE_ONLY=ON.

Embedding it pulls in no transitive dependency:

add_subdirectory(slipx)
target_link_libraries(your_simulator PRIVATE slipx::core)

step is const and stateless: no hidden state, no clock, no ambient RNG, no allocation. N instances parallelise trivially and snapshot/restore is a memcpy.

The stack

Dependencies point downward only, and tools/dep_lint.py fails the build if that stops being true.

slipx_registry   community parameter sets (data only, no code)          planned
slipx_id         system identification: manoeuvres, fitting, reports    planned
slipx_ros        ROS 2 wrapper: topics, TF, /clock, rosbag, launch      planned
slipx            Python package: bindings, car loader, run sinks        built
slipx_sim        orchestrator: N agents, fixed step, lockstep, replay   built
slipx_scene      track loading, BVH, contact, race control, events      planned
slipx_sense      sensor simulation: raycasting, noise, latency          planned
slipx_schema     JSON Schema definitions + reference parser             built
slipx_core       vehicle dynamics. The C++ standard library, nothing
                 else.                                                  built

The directories for the planned pieces exist and are empty. Nothing there half-works.

A car is a versioned directory: car.yaml as manifest, then dynamics.yaml, sensors.yaml, limits.yaml, provenance.yaml and a tyres/ directory of (compound, surface) pairs. car.yaml may also point at a URDF or xacro owning frames, link geometry and sensor mounts; it is optional today and becomes load-bearing when the ROS 2 layer needs a TF tree. Copy examples/cars/reference_1_10 to start one.

Not in scope

No photorealistic rendering, no camera model: SlipX is LiDAR-first and runs on CPU. That rules out a sensor, not a picture. A finished run renders to a self-contained animated SVG, provenance label and trajectory hash drawn into the image, with no dependency beyond the standard library and nothing drawn that the run did not record: no track, no car body.

No interactive viewer. SlipX owns the encoder rather than the viewer, so a run is emitted as MCAP and scrubbing a timeline is served by a tool built for it. A Rerun sink is available too; both encoders are optional extras rather than install requirements. The SVG sink needs no extra at all, and writes a file rather than opening a window, like every sink here.

No full-scale road vehicles, traffic or urban scenarios. No autonomy stack; the reference controllers exist to validate the sim, not to win with. No aerodynamics at 1/10 scale, where it is negligible below roughly 15 m/s. Collision physics will be plausible and deterministic, not fitted to data.

What can honestly be claimed

Verification runs in four layers.

Analytical: closed-form cases with known answers, which catches the sign and unit errors this kind of code actually has.
Invariant: energy and momentum conservation, left/right mirror symmetry asserted bit for bit, monotonicity under property-based sampling.
Cross-tier: the tiers must agree in the low lateral acceleration limit. For the reference car on an open differential, L1 and L2 agree on path radius to within 1% up to 0.82 g and part company by 0.85 g; the crossover is printed on every test run, plotted from the library and measured by an example. A locked rear axle disagrees from the first degree of steering instead, which is not a tyre effect: a single-track model has no differential to represent.
Empirical: missing. Until an outside contributor supplies a fitted parameter set with a validation report, the honest phrasing is physically structured and identifiable, not validated.

The first three are in place: 242 C++ tests and 195 Python tests, with two more modules that run only when the optional sink extras are installed, including an allocation counter proving step never touches the allocator. Every shipped parameter set carries a measured, identified or provisional label, and the tooling prints it rather than leaving it in the documentation.

Learning the subject

If the vocabulary here is new, there is a tutorial series on the concepts themselves, for somebody arriving from robotics or computer science rather than vehicle dynamics: Autonomous racing, from the ground up. Slip angles, load transfer, understeer and oversteer, the racing line, the g-g diagram, combined slip, differentials, and where a small electric car's acceleration and braking actually come from. A guide to the subject, not a manual for this library.

Where the reasoning lives

docs/adr has one numbered record per architectural decision, with what it costs: why there is no Eigen, why a missing tier raises, why diagnostics are NaN, why reference hashes are keyed by build. Most of the tempting simplifications have a record explaining what they break.

docs/reference is the lookup material: sign conventions, every parameter with its unit and the tier it first affects, the state and diagnostics blocks, the Python API, and the MF-lite derivation with worked numbers. Three runnable programs are in examples, and the test suite executes them rather than trusting them.

The component diagram is in docs/architecture/slipx.md and the release history in CHANGELOG.md.

The banner is output, not an illustration: docs/assets/make_banner.py rolls the car out of slipx_core at the double-track tier, and it drifts because its rear tyre is a lower-grip compound than its front. Every figure in the tutorial series is generated the same way; none of them carries a model of its own.

Licence

Apache-2.0.

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slipx 0.2.0

SlipX

Quickstart

Tiers

The tyre model

Determinism

Building from source

The stack

Not in scope

What can honestly be claimed

Learning the subject

Where the reasoning lives

Licence

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1 maintainer

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Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

Algorithm	Hash digest
SHA256	`e79231362491f9c10e327bbec4f7d7f409f86b7e670147f8b14dc78086bc4e04`
MD5	`7745fa4fb4515eda59f07d3124ce55e1`
BLAKE2b-256	`294e8b7ece170d2286f6c9f0f41392a63e88a7225d62337b1dcbba479677db49`

Algorithm	Hash digest
SHA256	`243bdc3d951dd26f2b095abad054d83245ed88da9f8f8cf2fff81a456d93a7ba`
MD5	`195fb70617f8bec88773fed5bfe03619`
BLAKE2b-256	`9604cac613830b06047625f236832ef3cc600782842782e6562d7a36cb25bccb`

Algorithm	Hash digest
SHA256	`24bf617eb627bdf346c7ed59fc00b37a52fa8baa1571bd38d5e285ff2512ce2f`
MD5	`880f8e833f313ccb83ff3aea1ce1a3d5`
BLAKE2b-256	`4b5437e7e0813b6b84ba98fa0ea407f4446b90bbd02952b596e65292e27082be`

Algorithm	Hash digest
SHA256	`4855e3b4153b3b35f27b33b5a144fdff79136eb5df07fd7e932b676dfbfaa6b9`
MD5	`343255f5fd3a9b25580240d6df3128d2`
BLAKE2b-256	`3bad8f0fa77b957d054dfc225582a5269ce20ecdac7e6d9c1ac516a4252c38b0`

Algorithm	Hash digest
SHA256	`c2195ee01f256a95920e6dd0a559b7e22261717b3b08780cc5512de0c87eefde`
MD5	`a9c38b5960683190ca170f8e18737c52`
BLAKE2b-256	`2ba62bf7fc1cf1dca94d46190b13fe53e39b4fbdd6ee6a3371809f7d7c72f193`