Technical challenges
The challenge for driverless car designers is to produce control systems
capable of analyzing sensory data in order to provide accurate detection
of other vehicles and the road ahead. Modern self-driving cars generally
use Bayesian simultaneous localization and mapping (SLAM) algorithms,
which fuse data from multiple sensors and an off-line map into current
location estimates and map updates. Waymo has developed a variant of
SLAM with detection and tracking of other moving objects (DATMO), which
also handles obstacles such as cars and pedestrians. Simpler systems may
use roadside real-time locating system (RTLS) technologies to aid
localization. Typical sensors include Lidar, stereo vision, GPS and IMU.
Udacity is developing an open-source software stack. Control systems on
automated cars may use Sensor Fusion, which is an approach that
integrates information from a variety of sensors on the car to produce a
more consistent, accurate, and useful view of the environment.
Driverless vehicles require some form of machine vision for the purpose
of visual object recognition. Automated cars are being developed with
deep neural networks, a type of deep learning architecture with many
computational stages, or levels, in which neurons are simulated from the
environment that activate the network. The neural network depends on an
extensive amount of data extracted from real-life driving scenarios,
enabling the neural network to “learn” how to execute the best course
of action.
In May 2018, researchers from MIT announced that they had built an
automated car that can navigate unmapped roads. Researchers at their
Computer Science and Artificial Intelligence Laboratory (CSAIL) have
developed a new system, called MapLite, which allows self-driving cars
to drive on roads that they have never been on before, without using 3D
maps. The system combines the GPS position of the vehicle, a “sparse
topological map” such as OpenStreetMap, (i.e. having 2D features of the
roads only), and a series of sensors that observe the road conditions.
Human factor challenges
Alongside the many technical challenges that autonomous cars face, there
exist many human and social factors that may impede upon the wider
uptake of the technology. As things become more automated, the human
users need to have trust in the automation, which can be a challenge in
itself.