Computer Vision for Automatic Toll Collection on Indian Expressways
Toll plazas have long been a source of queues on Indian highways, and electronic tags have already made payment faster. The next step under discussion is tolling that relies on cameras and positioning technology, potentially without vehicles needing to stop at all. This article explains how computer vision supports toll collection, how it relates to existing systems like FASTag, and the challenges involved.
From Cash Lanes to Electronic Tags
FASTag uses radio frequency identification. A sticker on the windscreen is read by an antenna as the vehicle passes through a lane, and the toll is deducted from a linked account. This reduced the need for cash transactions and shortened waiting times at many plazas.
However, tag-based systems still need lanes, barriers and readers. Problems such as damaged tags, insufficient balance or misread vehicles can still cause delays. Computer vision is increasingly used alongside tags to address these gaps and to enable more advanced tolling models.
How Computer Vision Fits In
Automatic number plate recognition, often called ANPR, is the core technology. High-speed cameras capture images of passing vehicles, and AI models locate the number plate, read its characters and match them to vehicle records.
Key Functions
- Verification: Confirming that the vehicle passing matches the vehicle linked to the tag, helping prevent misuse of tags.
- Vehicle classification: Identifying whether a vehicle is a car, light commercial vehicle, bus or multi-axle truck, which determines the toll rate.
- Exception handling: Recording vehicles that pass without a valid tag so the toll can be recovered later.
- Free-flow tolling: Supporting barrier-free gantries where vehicles are charged at highway speeds without stopping.
- Security and traffic data: Providing information useful for traffic studies and law enforcement where legally permitted.
Authorities have also discussed satellite-based tolling, in which onboard units use GNSS positioning to calculate charges based on distance travelled. Cameras would complement such systems by verifying vehicles and catching those without working units.
Challenges on Indian Roads
Number plates in India vary in condition and style. Dirt, damage, non-standard fonts and decorative designs can reduce recognition accuracy, though high security registration plates help standardise formats. AI models must be trained on local plate styles and perform well at night, in rain and in dusty conditions.
Lane discipline is another factor. In barrier-free systems, vehicles may pass gantries side by side or partially overlapping, so cameras need wide coverage and reliable tracking. Two-wheelers, which are generally exempt on expressways where permitted, must be correctly ignored.
Privacy and data protection are important considerations. Systems that capture and store images of every vehicle need clear rules on retention, access and use, consistent with India's data protection framework. Fair dispute processes are also needed when a plate is misread and a toll is charged incorrectly.
Looking Forward
Computer vision strengthens toll collection by verifying vehicles, classifying them accurately and enabling barrier-free tolling alongside FASTag and possible satellite-based systems. Its success depends on accurate recognition in local conditions, standardised number plates and strong safeguards for privacy and dispute resolution.