Autonomous Driving
Researchers and firms all over the globe are attempting to improve autonomous driving technology
with the primary objective of improving road safety and comfort. Autonomous vehicles (AVs) are
predicted to have a global influence in the future, affecting society, traffic safety, and transportation
infrastructure. The problem with autonomous vehicles is that technology has a long way to go before
securely driving humans in everyday situations. Meanwhile, it may result in the deaths of people, if
not robots. Even though having an accuracy of 99%, the technology has some limitations that may
result in undesirable circumstances. For instance, being in the self-driving car under a specific situation
where the vehicle has to decide whose lives should save in case of failure in the system, the people
inside the vehicle, or the people who may come into contact with the vehicle in that situation.
Applying the rules that will become the base for the entire situation will help get uninformative in the
whole process. These rules will support maintaining the same ground in the entire self-driving vehicle
industry. Such rules include technological safety, which has the error-handling capability for software
or hardware issues that may occur in the process. Other rules include the responsible balance of risks,
as the self-driving vehicle will decide based on their observations. This rule will help us answer the
factors on which the machine will make decisions and its risk allocation process. Also, it includes the
technique by which the technology is being integrated into the whole system. Moreover, various
other rules are Human agency, Responsibility, liability, accountability, privacy, and data governance.
According to the user's viewpoint towards the autonomous vehicle, the user is more inclined to have
an autonomous vehicle as it will help provide lane-keeping assistant and emergency braking. The user
is more attracted to having the autonomous vehicle features instead of viewing the bigger picture of
seeing the complexity of having in the actual society. Moreover, the bios ness of the user is more
inclined towards the way they treat the vehicle. Some think that having an autonomous vehicle is a
boon, whereas others believe it is a curse.
The alternative resolution to increase the efficiency and reliability of the self-driving vehicle is to
include intern–vehicle communication to improve the reliability. Also, an HD map is useful. HDMap is
a very accurate map in autonomous driving. It contains details not normally available on the traditional
map. These maps are very precise in order to predict distance. HD maps are captured using an array
of sensors like LIDARS, radars, digital cameras, and GPS. HD maps for self-driving cars usually include
map elements such as road shap, trffic sign and barriers.
LIDARS is currently used in AR technology to map the related object for computing the surrounding
layout, which is similar to the one used in the autonomous driving vehicle where the LIDAR is used to
map the surrounding car and their availabilities. Similarly, Argo AI announced the creation of a public
repository for its self-driving-car development data, which includes high-definition maps. The
Argoverse, as it's known, is a collection of statistics and maps gathered as part of Argo AI's
autonomous autonomy study, and it's available for free under a non-commercial Creative Commons
license. Researchers expect to be able to use these HD maps to develop AV technologies in the future.