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Transport, logistics and mobility
autonomous-trucking
autonomous trucking
Aurora
System
Techniques · 23
Primitives · 62
autonomous-trucking
Clock synchronization
distributed · medium: Estimate a clock's offset from four timestamps, filter out queueing noise, and steer the clock smoothly; NTP gets about a millisecond, PTP with hardware timestamps under a microsecond.
›
Fault tree analysis
embedded · medium: Write the unwanted top event as AND/OR logic over component failures, reduce it to minimal cut sets, then compute its probability and each failure's share.
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Safety monitor architecture
embedded · medium: Independent doer/checker safety monitor that forces a safe state when the main channel misbehaves
Multi-object tracking
machine-learning · medium: Turn per-frame detections (camera boxes or radar plots) into persistent IDs by predicting each track, gating, assigning and managing track births and deaths.
›
Object detection
machine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.
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Trajectory prediction
machine-learning · medium: Predicting multi-modal future paths of agents from history and map context
Camera calibration
robotics · medium: Intrinsic/extrinsic calibration (Zhang's method, distortion models, bundle adjustment)
Convolutional networks
ml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.
›
Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
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Extended Kalman filter
robotics · low: Kalman filter for nonlinear motion and measurement models: linearize f and h with Jacobians at the current estimate each step. The default estimator in robot navigation.
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Shared memory ipc
concurrency · low: Shared memory rings and futex signalling between processes
R-tree
geometry · low: A balanced tree of nested bounding boxes that indexes rectangles and points, answering window and nearest-neighbour queries by pruning whole subtrees.
›
Weight quantization
ml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.
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PID controller
robotics · low: Drive a measured value to a setpoint with three terms on the error: proportional, integral, derivative. The default feedback loop for motors, heaters, drones and gimbals.
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Phase-locked loop (digital)
signal-processing · low: Feedback loop that locks a local oscillator to the phase and frequency of an input tone; the core of carrier recovery, clock recovery and GNSS tracking.
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Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
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BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
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Binary decision diagrams
optimization · low: A reduced ordered BDD stores a Boolean function as a shared DAG that is unique per variable order, so equivalence is one integer compare and counting is one pass.
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End to end protection e2e
embedded · low: Per-message counter, ID and CRC over a black channel to detect loss, repetition, corruption, delay
Memory protection unit
embedded · low: A handful of base/size regions with permissions, checked on every access by the CPU, give microcontroller tasks isolation and stack-overflow traps without an MMU.
›
Redundancy voting
embedded · low: N-modular redundancy and MooN voting (2oo3, TMR) with discrepancy detection
Watchdog timer
embedded · low: A hardware down-counter that resets the chip unless software proves, in time and in a plausible pattern, that it is still healthy.
›
Non-maximum suppression
machine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.
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+ 16 more techniques
+ 46 more primitives
System
Techniques · 23
Primitives · 62
autonomous-trucking
Clock synchronization
distributed · medium: Estimate a clock's offset from four timestamps, filter out queueing noise, and steer the clock smoothly; NTP gets about a millisecond, PTP with hardware timestamps under a microsecond.
›
Fault tree analysis
embedded · medium: Write the unwanted top event as AND/OR logic over component failures, reduce it to minimal cut sets, then compute its probability and each failure's share.
›
Safety monitor architecture
embedded · medium: Independent doer/checker safety monitor that forces a safe state when the main channel misbehaves
Multi-object tracking
machine-learning · medium: Turn per-frame detections (camera boxes or radar plots) into persistent IDs by predicting each track, gating, assigning and managing track births and deaths.
›
Object detection
machine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.
›
Trajectory prediction
machine-learning · medium: Predicting multi-modal future paths of agents from history and map context
Camera calibration
robotics · medium: Intrinsic/extrinsic calibration (Zhang's method, distortion models, bundle adjustment)
Interacting multiple model filter
robotics · medium: IMM filter mixing several motion models for maneuvering targets
Model predictive control
robotics · medium: Each tick, solve a small optimization over a predicted horizon, apply only the first input, repeat. Handles constraints and multiple inputs that a PID loop cannot.
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Motion planning
robotics · medium: Planning collision-free trajectories (hybrid A*, RRT*, lattice planners, trajectory optimization)
Point cloud registration icp
robotics · medium: ICP/NDT alignment of point clouds with k-d trees for lidar localization
Sensor fusion
robotics · medium: Combining IMU, GNSS, camera, lidar and radar estimates with Kalman-type filters and time alignment
Slam
robotics · medium: Simultaneous localization and mapping: visual/lidar odometry, loop closure, pose-graph optimization
Assignment problem (Hungarian / Jon…
graphs · medium: Optimal one-to-one matching of rows to columns in O(n³) by growing a matching along shortest augmenting paths over dual potentials.
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Levenberg marquardt
numerics · medium: Damped Gauss-Newton nonlinear least squares for model calibration
Quadratic programming
optimization · medium: Convex QP solvers (active-set, interior-point, ADMM) for constrained optimization
Bundle adjustment
robotics · medium: Jointly refine camera poses and 3D points by sparse nonlinear least squares on reprojection error
Transform tree
robotics · medium: Time-indexed tree of coordinate frames with interpolated lookups between any two frames
Inverted index
storage · medium: Map each term to a sorted, compressed list of the documents containing it; the core of full-text search and other fast set queries.
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Interior-point method (primal-dual,…
optimization · medium: Follows the central path with Newton steps on the perturbed KKT system, one sparse Cholesky per iteration; Mehrotra's predictor-corrector makes it fast.
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Sparse direct solver
numerics · medium: Solve sparse Ax = b exactly by reordering, symbolic analysis, numeric LLᵀ/LU and triangular solves; fill-in, not flops per entry, decides the cost.
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Fill-reducing ordering
numerics · medium: Permute a sparse symmetric matrix before Cholesky/LU so the factor has few new nonzeros; minimum degree, nested dissection and RCM trade quality, speed and parallelism.
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Graph partitioning
graphs · medium: Split a graph into k balanced parts with few edges between them; multilevel coarsen-partition-refine (METIS, KaHIP) for quality, streaming heuristics (LDG, Fennel) for one-pass scale.
›
Convolutional networks
ml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.
›
Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
›
Extended Kalman filter
robotics · low: Kalman filter for nonlinear motion and measurement models: linearize f and h with Jacobians at the current estimate each step. The default estimator in robot navigation.
›
Shared memory ipc
concurrency · low: Shared memory rings and futex signalling between processes
R-tree
geometry · low: A balanced tree of nested bounding boxes that indexes rectangles and points, answering window and nearest-neighbour queries by pruning whole subtrees.
›
Weight quantization
ml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.
›
PID controller
robotics · low: Drive a measured value to a setpoint with three terms on the error: proportional, integral, derivative. The default feedback loop for motors, heaters, drones and gimbals.
›
Phase-locked loop (digital)
signal-processing · low: Feedback loop that locks a local oscillator to the phase and frequency of an input tone; the core of carrier recovery, clock recovery and GNSS tracking.
›
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
›
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
›
Binary decision diagrams
optimization · low: A reduced ordered BDD stores a Boolean function as a shared DAG that is unique per variable order, so equivalence is one integer compare and counting is one pass.
›
End to end protection e2e
embedded · low: Per-message counter, ID and CRC over a black channel to detect loss, repetition, corruption, delay
Memory protection unit
embedded · low: A handful of base/size regions with permissions, checked on every access by the CPU, give microcontroller tasks isolation and stack-overflow traps without an MMU.
›
Redundancy voting
embedded · low: N-modular redundancy and MooN voting (2oo3, TMR) with discrepancy detection
Watchdog timer
embedded · low: A hardware down-counter that resets the chip unless software proves, in time and in a plausible pattern, that it is still healthy.
›
Non-maximum suppression
machine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.
›
Kalman filter
robotics · low: Optimal recursive estimator for a linear system with Gaussian noise: fuses a motion model with noisy measurements into a state estimate plus its covariance.
›
Data augmentation
machine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.
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Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Recurrent networks
machine-learning · low: GRU and LSTM cells for streaming sequence models
Homogeneous transforms
geometry · low: 4x4 affine and projective matrices, homogeneous coordinates, view and projection, normal matrices
Lens distortion correction
graphics · low: Pre-warp rendered frames for headset optics: barrel distortion mesh and chromatic aberration
PCA and SVD
machine-learning · low: Find orthogonal directions of maximum variance by an SVD of the centred data; randomized SVD gets the top k components in a few passes.
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Fixed-rate control loop
embedded · low: Run a controller at an exact period using absolute deadlines and a fixed dt, so jitter never accumulates and the math stays valid.
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Runge-Kutta 4
numerics · low: The classic four-stage, fourth-order ODE step; error drops 16× when the step halves, with no derivatives or past steps needed.
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k-d tree
geometry · low: A binary tree that splits k-dimensional points by one coordinate per level, giving fast nearest-neighbour and box queries on static low-dimensional data.
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A* search
graphs · low: Dijkstra ordered by cost-so-far plus an admissible estimate of the remaining cost; optimal paths while exploring far fewer nodes toward the goal.
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PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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GJK and EPA
physics · low: GJK decides whether (and how far apart) two convex shapes are from their support functions alone; EPA expands its simplex into penetration depth and normal.
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Occupancy grid
robotics · low: Probabilistic grid map updated with log-odds from range sensors
Kabsch rmsd alignment
geometry · low: Optimal rigid superposition of point sets by SVD; RMSD computation
Ransac
robotics · low: Random sample consensus for robust model fitting with outliers (homographies, planes, poses)
SPSC ring buffer
data-structures · low: Bounded wait-free queue for exactly one producer and one consumer thread, built from an array and two indices with acquire/release ordering.
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Complementary filter
robotics · low: Blend high-passed gyro integration with low-passed accelerometer/magnetometer attitude (Mahony/Madgwick)
Feature detection orb
robotics · low: FAST corners with oriented BRIEF descriptors and Hamming matching for tracking and relocalization
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
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Dijkstra's algorithm
graphs · low: Single-source shortest paths with non-negative edge weights by settling vertices in order of distance with a priority queue.
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Finite differences
numerics · low: Replace derivatives with weighted differences of grid values; simple stencils plus a stability condition (CFL) turn PDEs like heat, advection and waves into array loops.
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LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
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Rigid-body transforms (SE(3) poses)
robotics · low: Represent, compose, invert, integrate and interpolate 3D poses as unit quaternion plus translation, with exp/log maps for small motions and uncertainty.
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Quaternion rotations
geometry · low: Unit quaternions as 3D rotations: 4 floats, cheap composition, no gimbal lock, plus the conversions, interpolation choices and numerical care needed in practice.
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Lowest common ancestor
graphs · low: LCA/merge-base computation in trees and DAGs
Spline interpolation
numerics · low: Linear, bilinear/trilinear and cubic spline interpolation on grids and tables
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
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Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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BM25 ranking
search-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.
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Sorted-list intersection
search-sort · low: Intersect sorted id lists by merging, galloping from the short list into the long one, and skipping whole compressed blocks, shortest list first.
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Newton root finding
numerics · low: Linearize at the guess and jump to the tangent root; quadratic convergence near a simple root, made robust by brackets (1D) or line search.
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False sharing
concurrency · low: Independent variables written by different cores on one cache line force the line to bounce between cores; pad or regroup hot data to fix it.
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Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
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Dma transfers
embedded · low: DMA controllers: descriptors, circular buffers, cache coherence for zero-CPU data movement
PWM generation
embedded · low: A timer's counter, period and compare registers draw the pulse train; preload, dead time and DMA make duty changes glitch-free, shoot-through-free and cheap.
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CPU caches and the memory hierarchy
hardware-os · low: Sizes, line widths and latencies of L1/L2/L3/DRAM on modern x86 and Apple M-series, and the data-layout rules that follow from them.
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SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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Text normalization
machine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.
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Explicit Euler
numerics · low: Step an ODE forward by its current slope, y += h·f(t, y); first-order, one evaluation per step, the baseline every other integrator is measured against.
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Low precision number formats
numerics · low: Bf16, fp16, fp8 (E4M3, E5M2), MX block formats and int4: ranges, rounding, scaling
Thomas tridiagonal solver
numerics · low: O(n) tridiagonal linear solve used in implicit finite-difference and spline schemes
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
›
Unicode text tokenization for search
search-sort · low: Turn multilingual text into index terms: segment words (UAX 29 style, CJK bigrams), normalize, case-fold and accent-fold, identically at index and query time.
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Copy for AI
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