Showing posts with label self driving. Show all posts
Showing posts with label self driving. Show all posts

Artificial Intelligence - AI Systems That Are Autonomous Or Semiautonomous.


Autonomous and semiautonomous systems are characterized by their decision-making dependence on external orders.

They have something in common with conditionally autonomous and automated systems.

Semiautonomous systems depend on a human user somewhere "in the loop" for decision-making, behavior management, or contextual interventions, while autonomous systems may make decisions within a defined region of operation without human input.

Under some situations, conditionally autonomous systems may operate independently.

Automated systems differ from semiautonomous and autonomous systems (autonomy) (automation).

The actions of the earlier systems are preset sequences directly related to specific inputs, while the later systems' actions are predefined sequences directly tied to specified inputs.

When a system's actions and possibilities for action are established in advance as reactions to certain inputs, it is termed automated.

A garage door that automatically stops closing when a sensor detects an impediment in its path is an example of an automated system.

Sensors and user interaction may both be used to collect data.

An automated dishwasher or clothes washer, for example, is a user-initiated automatic system in which the human user sets the sequences of events and behaviors via a user interface, and the machine subsequently executes the commands according to established mechanical sequences.

Autonomous systems, on the other hand, are ones in which the capacity to evaluate conditions and choose actions is intrinsic to the system.

The autonomous system, like an automated system, depends on sensors, cameras, or human input to give data, but its responses are marked by more complicated decision-making based on the contextual evaluation of many simultaneous inputs such as user intent, environment, and capabilities.

When it comes to real-world instances of systems, the terms automated, semiautonomous, and autonomous are used interchangeably depending on the nature of the tasks at hand and the intricacies of decision-making.

These categories aren't usually defined clearly or exactly.

Finally, the degree to which these categories apply is determined by the size and scope of the activity in question.

While the above-mentioned basic differences between automated, semiautonomous, and autonomous systems are widely accepted, there is some dispute as to whether these system types exist in real systems.

The degrees of autonomy established by SAE (previously the Society of Automotive Engineers) for autonomous automobiles are one example of such ambiguity.

Depending on road or weather conditions, as well as situational indices like the existence of road barriers, lane markings, geo-fencing, adjacent cars, or speed, a single system may be Level 2 partly autonomous, Level 3 conditionally autonomous, or Level 4 autonomous.

The degree of autonomy may also be determined by how an automobile job is characterized.

In this sense, a system's categorization is determined as much by its technical structure as by the conditions of its operation or the characteristics of the activity focus.


E Vehicles that are self-driving.

 The contrasts between automated, semiautonomous, conditionally autonomous, and completely autonomous vehicle systems are shown using automated, semiautonomous, conditionally autonomous, and fully autonomous car systems.

Automated technology, like as cruise control, is an example.

The user specifies a vehicle speed goal, and the vehicle maintains that speed while adjusting acceleration and deceleration as needed by the terrain.

However, in the case of semiautonomous vehicles, a vehicle may be equipped with an adaptive cruise control feature (one that regulates a vehicle's speed in relation to a leading vehicle and to a user's input), as well as lane keeping assistance, automatic braking, and collision mitigation technology.

Semiautonomous cars are now available on the market.

Many possible inputs (surrounding cars, lane markings, human input, impediments, speed restrictions, etc.) may be interpreted by systems, which can then regulate longitudinal and latitudinal control to semiautonomously direct the vehicle's trajectory.

The human user is still involved in decision-making, monitoring, and interventions in this system.

Conditional autonomy refers to a system that allows a human user to "leave the loop" of control and decision-making under certain situations.

The vehicle analyzes emergent inputs and controls its behavior to accomplish the objective without human supervision or intervention after a goal is set (e.g., to continue on a route).

Internal to the activity (defined by the purpose and accessible methods), behaviors are governed and controlled without the involvement of the human user.

It's crucial to remember that every categorization is conditional on the aim and activity being operationalized.

Finally, an autonomous system has fewer constraints than conditional autonomy and is capable of controlling all tasks in a given activity.

An autonomous system, like conditional autonomy, functions inside the activity structure without the involvement of a human user.

Autonomous Robotics

For a number of reasons, autonomous systems may be found in the area of robotics.

There are a variety of reasons why autonomous robots should be used to replace or augment humans, including safety (for example, spaceflight or planetary surface exploration), undesirable circumstances (monotonous tasks such as domestic chores and strenuous labor such as heavy lifting), and situations where human action is limited or impossible (search and rescue in confined conditions).

Robotics applications, like automobile applications, may be deemed autonomous within the confines of a carefully defined domain or activity area, such as a factory assembly line or a residence.

The degree of autonomy, like autonomous cars, is dependent on the specific area and, in many situations, excludes maintenance and repair.

Unlike automated systems, however, an autonomous robot inside such a defined activity structure would behave to achieve a set objective by sensing its surroundings, analyzing contextual inputs, and regulating behavior appropriately without the need for human interaction.

Autonomous robots are now used in a wide range of applications, including domestic uses such as autonomous lawn care robots and interplanetary exploration applications such as the Mars rovers MER-A and MER-B.

Semiautonomous Weapons

 is an acronym for "Semiautonomous Weapons." As part of contemporary military capabilities, autonomous and semiautonomous weapon systems are now being developed.

The definition of, and difference between, autonomous and semiautonomous changes significantly depending on the operationalization of the terminology, the context, and the sphere of activity, much as it does in the preceding automobile and robotics instances.

Consider a landmine as an example of an automated weapon that is not self-contained.

It reacts with fatal force when a sensor is activated, and there is no decision-making capabilities or human interaction.

A semiautonomous system, on the other hand, processes inputs and acts on them for a collection of tasks that form weaponry activity in collaboration with a human user.

The weapons system and the human operator must work together to complete a single task.

To put it another way, the human user is "in the know." Identifying a target, aiming, and shooting are examples of these activities.

Navigation toward a target, placement, and reloading are all possible.

These duties are shared between the system and the human user in a semiautonomous weapon system.

An autonomous system, on the other hand, would be accountable for all of these duties without the need for human monitoring, decision-making, or intervention after the objective was determined and the parameters provided.

There are presently no completely autonomous weapons systems that meet these requirements.

These meanings, as previously stated, are technologically, socially, legally, and linguistically dependent.

The distinction between semiautonomous and autonomous systems has ethical, moral, and political implications, particularly in the case of weapons systems.

This is particularly relevant for assessing accountability, since causal agency and decision-making may be distributed across developers and consumers.

As in the case of machine learning algorithms, the sources of agency and decision-making may also be ambiguous.



The various obstacles in building optimum user interfaces for semiautonomous and autonomous systems are mirrored in the ambiguity of their definitions.

For example, in the case of automobiles, ensuring that the user and the system (as designed by the system's designers) have a consistent model of the capabilities being automated (as well as the intended distribution and degree of control) is crucial for the safe transfer of control responsibility.

In the sense that once an activity area is specified, control and responsibility are binary, autonomous systems pose similar user-interface issues (either the system or the human user is responsible).

The problem is reduced to defining the activity and relinquishing control in this case.

Because the description of an activity domain has no required relationship to the composition, structure, and interaction of constituent activities, semiautonomous systems create more difficult issues for the design of user interfaces.

Particular tasks (such as a car maintaining lateral position in a lane) may be decided by an engineer's use of specific technical equipment (and the restrictions that come with it) and therefore have no link to the user's mental representation of that work.

An obstacle detection task, in which a semiautonomous system moves about an environment by avoiding impediments, is an example.

The machine's obstacle detection technologies (camera, radar, optical sensors, touch sensors, thermal sensors, mapping, and so on) define what is and isn't an impediment, and such restrictions may be opaque to the user.

As a consequence of the ambiguity, the system must communicate with a human user when assistance is required, and the system (and its designers) must recognize and anticipate any conflict between system and user models.

Other considerations for designing semiautonomous and autonomous systems (specifically in relation to the ethical and legal dimensions complicated by the distribution of agency among developers and users) include identification and authorization methods and protocols, in addition to the issues raised above.

The difficulty of identifying and approving users for autonomous technology activation is crucial since once activated, systems no longer need continuous monitoring, intermittent decision-making, or interaction.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Autonomy and Complacency; Driverless Cars and Trucks; Lethal Autonomous Weapons Systems.

Further Reading

Antsaklis, Panos J., Kevin M. Passino, and Shyh Jong Wang. 1991. “An Introduction to Autonomous Control Systems.” IEEE Control Systems 11, no. 4 (June): 5–13.

Bekey, George A. 2005. Autonomous Robots: From Biological Inspiration to Implementation and Control. Cambridge, MA: MIT Press.

Norman, Donald A., Andrew Ortony, and Daniel M. Russell. 2003. “Affect and Machine Design: Lessons for the Development of Autonomous Machines.” IBM Systems Journal 42, no. 1: 38–44.

Roff, Heather. 2015. “Autonomous or ‘Semi’ Autonomous Weapons? A Distinction without a Difference?” Huffington Post, January 16, 2015.

SAE International. 2014. “Taxonomy and Definitions for Terms Related to On-Road 
Motor Vehicle Automated Driving Systems.” J3016. SAE International Standard.

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