Multi-Lane Free-Flow (MLFF) tolling only works when imaging stays sharp at speed, across lanes, and through day-night shifts. Toll operators want transponder-free flow, accurate capture, and simple roadside installs that run on solar. Missing even a single event can ripple into billing gaps, dispute overhead, and traffic slowdowns.
A leading tolling technology provider approached e-con Systems to modernize its MLFF rollout. The client’s requirement was clear. Their systems needed high-performance imaging, low power consumption, and consistent visibility in changing light. It also had to work smoothly with the client’s Raspberry Pi setup, deploy fast at the roadside, and stay stable in solar-powered environments.
This project’s journey kicked off by identifying the primary challenges.
What Were the Primary Challenges Faced By the Client?
- Accurate capture of fast-moving vehicles, covering multiple lanes in MLFF conditions
- Consistent imaging through variable light and weather, around the clock
- Low-power operation for solar-based sites and compact roadside enclosures
- Clean integration with Raspberry Pi hardware and existing tolling backend
- Frame-level correlation between imagery and tolling events
Powering Next-Generation Multi-Lane Free-Flow (MLFF) Tolling with End-to-End Vision Solutions
e-con Systems’ AI Vision Solution at a Glance
e-con Systems developed a high-performance, power-efficient imaging solution for real-time vehicle detection and monitoring in MLFF environments. The highlights of the solution included:
- Camera hardware: The Sony Pregius S global shutter camera with motorized zoom optics captured fast-moving vehicles and came with a mechanical IR cut filter, ambient light sensor, and strobe output.
- Video encoder design: The Ambarella-based encoder supported dual-stream output (MJPEG + H.264/H.265) to optimize bandwidth and kept power consumption near 5 watts.
- Time stamping and synchronization: Frame-level timestamps with approximately 10 ms resolution ensured precise event mapping, and NTP synchronization maintained camera alignment.
- AI-compatible software: The RTSP-based streaming stack with control APIs enabled AI inference directly on Raspberry Pi Compute Modules.
- Seamless integration: The complete setup worked with the client’s existing Raspberry Pi and solar infrastructure.
What Was the Impact of e-con Systems’ AI Vision Solution?
- Simplified tolling with fewer infrastructure needs – reduced civil work and quicker rollouts
- Low-power envelope for solar sites – shortened deployment timelines at remote or constrained locations
- >99.95% detection and classification accuracy in the field, even with changing light or weather
- Support for vehicle occupancy detection, speed and violation analysis, dynamic pricing, and reservation-based lane allocation
Want the full breakdown of this custom multi-camera solution?
Read the full case study to see how e-con Systems delivered a next-gen imaging system that redefines real-time accuracy for Multi-Lane Free-Flow (MLFF) tolling.
If you’re currently exploring imaging solutions for your tolling or any smart traffic system, our experts can guide you to the right fit. Please get in touch at camerasolutions@e-consystems.com.
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Dilip Kumar is a computer vision solutions architect having more than 8 years of experience in camera solutions development & edge computing. He has spearheaded research & development of computer vision & AI products for the currently nascent edge AI industry. He has been at the forefront of building multiple vision based products using embedded SoCs for industrial use cases such as Autonomous Mobile Robots, AI based video analytics systems, Drone based inspection & surveillance systems.