Autonomiczna jazda

Autonomous driving · id autodriving · aliases: self-driving cars, autonomous vehicles, autonomous driving systems, samochody autonomiczne, pojazdy autonomiczne, auta bez kierowcy, robotaxi

Dziedzina łącząca percepcję, mapy, prognozowanie ruchu, planowanie i symulację, aby pojazd samodzielnie prowadził się w ruchu drogowym.

The field combining perception, maps, motion forecasting, planning and simulation so that a vehicle drives itself in road traffic.

In one sentence

Samochody, które same widzą drogę, przewidują ruch innych i decydują, jak jechać, bez człowieka za kierownicą.

Cars that see the road, anticipate what others will do and decide how to drive, with nobody at the wheel.

Problem it solves

Ludzie za kierownicą męczą się, rozpraszają i popełniają błędy, a wielu w ogóle nie może prowadzić.

People behind the wheel get tired, distracted and make mistakes, and many cannot drive at all.

What it does

Działa jak kierowca z oczami dookoła głowy: łączy sygnały z wielu czujników, zgaduje zamiary innych i co chwilę poprawia plan.

It works like a driver with eyes all around their head: it merges many sensors, guesses others' intentions and keeps revising its plan.

Everyday example

Taksówka bez kierowcy podjeżdża pod dom, omija rowerzystę na pasie i zatrzymuje się, gdy pieszy wchodzi na przejście.

A driverless taxi pulls up at your door, steers around a cyclist in the lane and stops when someone steps onto the crossing.

This concerns you if you…

• jeździsz autem, które samo trzyma pas lub odstęp
• zastanawiasz się, kiedy auta pojadą zupełnie same
• pracujesz przy robotach, dronach lub transporcie
• drive a car that keeps its lane or distance by itself
• wonder when cars will drive entirely on their own
• work with robots, drones or transport

Subtopics

• Jazda end-to-end (End-to-end autonomous driving): An approach where perception, prediction and planning form one network trained jointly, from sensor data to trajectory.
  • Modele wizja–język–akcja za kierownicą (Vision-language-action models for driving): Multimodal models adapted to driving that use images and instructions to predict trajectories, objects or road elements as text or action tokens.
  • Wspólne uczenie percepcji, predykcji i planowania (Joint perception, prediction and planning): End-to-end architectures with explicit stages (tracking, mapping, prediction, occupancy, planning) linked by queries and trained jointly for planning.
• Mapy i lokalizacja pojazdu (Maps and localisation for autonomous driving): Lane-accurate representation of the road network and estimation of the vehicle's pose relative to it from onboard sensors.
  • Lokalizacja pojazdu na mapie (Map-based vehicle localisation): Estimating the vehicle's position and orientation by fusing GNSS, inertial sensors, odometry and matching observations to a reference map.
  • Mapy budowane w locie z czujników (Online vectorised HD map construction): Predicting vector map elements (lane lines, road boundaries, crossings) in a top-down view directly from the vehicle's sensors.
• Percepcja otoczenia pojazdu (Perception for autonomous driving): Detecting, tracking and describing objects and free space around the vehicle in 3D from cameras, LiDAR and radar.
  • Fuzja kamer, LiDAR-u i radaru (Camera–LiDAR–radar sensor fusion): Combining camera, LiDAR and radar data into one scene representation, often in a shared bird's-eye view (BEV).
  • Siatki zajętości 3D wokół pojazdu (3D occupancy prediction): Predicting for every voxel around the vehicle whether it is occupied and which semantic class it has, instead of object boxes alone.
  • Widok z lotu ptaka z wielu kamer (BEV) (Bird's-eye-view perception from multi-camera images): Transforming features from multiple camera images into a shared top-down grid on which detection, segmentation and planning operate.
• Planowanie ruchu i sterowanie pojazdem (Motion planning and vehicle control): Choosing the vehicle's manoeuvre and trajectory with respect to obstacles, rules and comfort, and executing it with a controller.
  • Klasyczne planowanie trajektorii (Classical trajectory planning): Planning a driving path with an explicit motion model, search, sampling or optimisation and a hand-defined cost function.
  • Planery uczone na nagraniach jazdy (Learning-based motion planning): Trajectory planners trained by imitating human driving, often with extra safety losses and perturbed data.
• Przewidywanie ruchu innych uczestników ruchu (Motion forecasting for road agents): Predicting road users' future trajectories a few seconds ahead from motion history, the map and interactions; a model may return several alternatives.
  • Kodowanie sceny drogowej jako grafu wektorów (Vectorised scene encoding): Representing lanes, markings and agent tracks as vector polylines processed by a hierarchical graph network or transformer.
  • Przewidywanie kilku wariantów ruchu naraz (Multi-modal trajectory prediction): Generating a set of distinct trajectories with probabilities for each agent, covering different intentions rather than their average.
• Symulacja i walidacja bezpieczeństwa jazdy (Driving simulation and safety validation): Simulation environments, metrics and processes (scenario testing, standards, on-road statistics) for assessing driving safety and quality.
  • Generatywne modele świata do jazdy (Generative world models for driving): Generative models predicting future video frames or sensor states, conditioned on the vehicle's actions and text descriptions.
  • Odtwarzanie scen z nagrań do symulacji czujników (Neural sensor simulation from driving logs): Reconstructing scenes from driving logs with neural representations and rendering camera and LiDAR data from new viewpoints for closed-loop simulation.
  • Symulacja i benchmarki planowania jazdy (Driving simulation and planning benchmarks): Environments and benchmarks that evaluate planners either in reactive simulation or on logs with a simplified model of decision outcomes.
  • Testy scenariuszowe i normy bezpieczeństwa (SOTIF) (Scenario-based testing and safety standards (SOTIF)): Methods and standards for building evidence about driving-system safety: defining operating conditions, scenario tests, analysing functional insufficiencies and using on-road data.

Sources

• Self-Driving Cars Specialization (University of Toronto, Coursera) · Steven Waslander, Jonathan Kelly / University of Toronto
• End-to-end Autonomous Driving: Challenges and Frontiers · Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, Hongyang Li
• Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles · Kristofer D. Kusano, John M. Scanlon, Yin-Hsiu Chen, Timothy L. McMurry, Ruoshu Chen, Tilia Gode, Trent Victor (Waymo) · sponsored