The Role of Sensors in Autonomous Vehicles and its Impact on Smart Transportation Systems
Once at the forefront of a transformation in the transportation industry, autonomous vehicles have all but disappeared from our collective discourse. Most autonomous vehicles depend on sensors to detect what is around and make some decisions without human interference. Sensors are paramount in functioning AVs since they give real-time information regarding the vehicle's location, rate, and surrounding objects. The sensors here are for all purposes; some use cameras, while others use lidar, radar, and several ultrasonic sensors. Combining both gives the vehicle a 360-degree view and helps it maneuver safely through even the most complex environments. AV-cum-smart transport systems can help improve traffic flow and road safety and increase overall transportation efficiency. Sensors are critical to harnessing these benefits and allow the AVs to work by way of other vehicles or organization, such as circulation signals and road ciphers. This communication gets dynamic route planning so that the time of travel and fuel can be saved. Nevertheless, several challenges remain to be addressed with sensor reliability under any conditions; however adverse, they may still face serious opposition and cybersecurity. Further research and development in the area of sensor technology are imperative to bring AVs into common use, as well as for using smart transportation systems effectively.
Authors
- Aneesh Wunnava (ORCID: https://orcid.org/0000-0002-3869-1324)
- S. Lalithakumari
- Ashutosh Pattanaik
- Lalit Khanna
- Hitesh Kalra (ORCID: https://orcid.org/0009-0000-5064-2165)
- Rakesh Kumar Yadav (ORCID: https://orcid.org/0000-0002-7387-5903)
Institutions
- Jain University (IN)
- Siksha O Anusandhan University (IN)
- Maharishi University of Management and Technology (IN)
- Sathyabama Institute of Science and Technology (IN)
- Chitkara University (IN)
Publication Details
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1109/autocom64127.2025.10956194
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00