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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or outdated data can lead to incorrect decisions that could cause accidents or other issues.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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