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Topic Review
Autonomous Navigation
Autonomous navigation is a very important area in the huge domain of mobile autonomous vehicles.  Sensor integration is a key concept that is critical to the successful implementation of navigation.  As part of this publication, we review the integration of Laser sensors like LiDAR with vision sensors like cameras.  The past decade, has witnessed a surge in the application of sensor integration as part of smart-autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, disinfecting hospitals post Corona virus treatments, driver-less vehicles, sanitizing public areas, smart systems to detect deformation of road surfaces, to name a handful.  These smart systems are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for integration of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement integration of multimodal data from different types of sensors like LiDAR that use light scan technology, various types of Red Green Blue (RGB) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation
  • 4.4K
  • 30 Oct 2020
Topic Review
Autonomous Navigation of Agricultural Robots
Regarding agricultural harvesting robots, they typically consist of mobile platforms carrying robotic arms. These robots require advanced vision systems, employing adaptive thresholding algorithms, as well as texture-based methods and color shape characteristic extraction, to identify target fruits.
  • 152
  • 23 Jan 2024
Topic Review
Autonomous Navigation of Robots
In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning.
  • 392
  • 30 Aug 2023
Topic Review
Autonomous Ride-Sharing Service using Graph Embedding
Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first–last-mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride Problem with certain features.
  • 121
  • 26 Feb 2024
Topic Review
Autonomous Vehicle
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving operations and functions without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors.  An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task.  Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030. 
  • 938
  • 17 Feb 2021
Topic Review
Autonomous Vehicle and Adverse Weather Conditions
The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been observed that currently deployed AVs, like human drivers, struggle the most in cases of adverse weather conditions, unsignalized intersections, crosswalks, roundabouts, and near-accident scenarios.
  • 372
  • 06 Jul 2023
Topic Review
Autonomous Vehicle Decision-Making for Handling a Round Intersection
Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging because it is more difficult to perceive the other vehicles in and near the intersection. 
  • 333
  • 01 Dec 2023
Topic Review
Autonomous Vehicle Guideline for Public Road-Testing Sustainability
Numerous countries have developed guidelines for public road testing, but those rules are not uniform, and discrepancies occur between nations. Issues such as vehicular safety, registrations, authority, insurance, cybersecurity, and infrastructures weigh differently in each country. Rather than relying on a single national standard as a reference, an amalgam of guidelines from different countries allows a more holistic and measured view of AV testing practices. Synthesizing these diverse national regulations into global guidelines would promote the safety and sustainability of autonomous vehicle testing and benefit all parties interested in autonomous vehicles.
  • 1.8K
  • 17 Feb 2022
Topic Review
Autonomous Vehicle Localization
In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements.
  • 146
  • 29 Dec 2023
Topic Review
Autonomous Vehicle Vulnerabilities
Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety.
  • 358
  • 18 Aug 2023
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