title: Kalit so‘zlar - Qarshi davlat universiteti international scientific and practical conference on algorithms and current...
text: Kalit so‘zlar:
  Sun'iy intellekt, haydash sxemalarini optimallashtiring, tizim aniqlaydi,
  svetofor.
  Artificial intelligence (AI) is revolutionizing the automotive industry by enabling self-
  driving cars and AI-assisted driving systems. One of the key benefits of integrating AI in the
  automotive sector is the ability to control the driver on the road. AI can monitor the
  driver's behavior, optimize driving patterns, and improve the overall driving experience.
  This article explores the use of AI in controlling the driver on the road and the advantages
  and limitations of this technology. Advantages of AI in Controlling Drivers on the Road:
  Safety: AI can monitor the driver's behavior and detect signs of drowsiness, distraction,
  or impairment. It can then take corrective actions such as alerting the driver or taking
  control of the vehicle to prevent accidents. AI can also improve road safety by detecting
  and responding to potential hazards, such as pedestrians or other vehicles.
  Efficiency: AI can optimize driving patterns, reduce fuel consumption, and emissions. It
  can also optimize traffic flow, reduce congestion, and travel times.
  Personalization: AI can learn the driver's preferences and provide personalized
  recommendations for music, temperature, and other settings, enhancing the overall driving
  experience.
  Limitations and Challenges:
  Data Accuracy: AI requires accurate and up-to-date data to make decisions. Inaccurate
  or outdated data can lead to incorrect decisions that could cause accidents or other issues.
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  Human Oversight: AI still requires human oversight to ensure that it is functioning
  correctly and making decisions that are in the best interest of the driver and other road
  users.
  Figure 1 presents a flow chart outlining the project's steps, which can be summarized as
  follows: input the road speed into the transmitter and send it to the car's receiver; the car
  receives and computes its own speed; the car's PIC compares the two speeds; if the car's
  speed is less than the road speed, the display shows "normal speed"; if the car's speed is
  higher than the road speed, the driver receives three alarms. If the driver does not reduce
  their speed, the PIC will issue a fine [1].
  Ethical Considerations: AI must be programmed with ethical and moral considerations
  to ensure that it does not harm the driver or other road users.
  Figure 1. Flow chart diagram
  As previously discussed, the transmitting circuit generates a specific frequency based on
  the road speed value entered via the keypad. The receiver then receives this frequency, and
  we can determine the road speed using it. To convert the received frequency, we connect
  the receiver to an interfacing PIC, which acts as the decoder for this portion of the
  demodulation system, as depicted in the receiving circuit diagram. Subsequently, the PIC
  measures the frequency using an appropriate code and applies the following equation:
  ,
  (1)
  The equation for determining road speed is represented by "f" (frequency in Hz) and
  "sr" (road speed in km/h). This equation is programmed into the PIC's code. Upon
  executing the code, the road speed we transmitted is received, and the result is displayed
  on the LCD. Upon receiving the frequency, the PIC will calculate the road speed using the
  following process:
  One of the most promising applications of AI in controlling drivers on the road is in
  driver assistance systems. These systems use sensors and cameras to monitor the driver's
  behavior and provide feedback in real-time. For example, if the system detects that the
  driver is becoming drowsy or distracted, it can sound an alarm or vibrate the steering
  wheel to alert the driver. Similarly, if the system detects that the driver is swerving or
  braking too hard, it can take control of the vehicle and bring it to a safe stop. Another
  promising application of AI in controlling drivers on the road is in autonomous vehicles.
  They can detect and respond to traffic signals, other vehicles, and pedestrians, making
  driving safer and more efficient. AI-based systems can also be used for traffic management,
  helping to reduce congestion and improve traffic flow. For example, traffic lights can be
  equipped with sensors and cameras that monitor traffic patterns in real-time. Machine
  learning algorithms can then analyze this data to optimize the timing of the traffic lights,
  reducing wait times and improving traffic flow. Despite the potential of AI-based systems
  for controlling drivers on the road, there are also some challenges that need to be
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  addressed. One of the main challenges is ensuring the safety and reliability of these
  systems.
  AI-based systems must be thoroughly tested and validated before they can be deployed
  on a large scale. There is also a need for regulations and standards to ensure that these
  systems are designed and implemented in a safe and responsible manner.
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