How does a forward-facing camera work?

They capture key elements like lanes, vehicles, pedestrians, traffic signs, and obstacles, which the ADAS processes to assist in making real-time decisions. These decisions support various functions, such as lane-keeping, object detection, and collision avoidance. It contributes to the broader goal of autonomous mobility and empowers vehicles to operate with minimal human intervention.

In this blog, you'll learn how forward-facing cameras work in ADAS and their top use cases.

How does a forward-facing camera work in ADAS?

High-resolution forward-facing cameras with narrow-angle lenses provide zoomed-in views for vehicles to focus on distant objects. This helps detect pedestrians, vehicles, or obstacles from distances of 50-100 metres, where wide-angle lenses may miss crucial details.

How does this work? The camera continuously captures images, which are processed in real time by the vehicle's onboard system. Algorithms then work to identify and differentiate between various obstacles, as machine learning models improve the accuracy of this process by relying on large amounts of training data. These models are designed to identify and categorise objects like vehicles, pedestrians, and cyclists, helping the system make better decisions while driving.

Furthermore, in practical driving conditions, the imaging quality of application can be impacted by varying light levels throughout the day or interference from artificial lighting. To mitigate, HDR (High Dynamic Range) technology enables the camera to handle extreme light contrasts so that bright and shadowed areas are clearly captured. Moreover, flickering from LED streetlights or headlights often distort images. However, it can be overcome with LFM (LED Flicker Mitigation) technology, which reduces these distortions to ensure an uninterrupted feed.

ADAS-based use cases of forward-facing cameras

Accurate traffic sign recognition

Forward-facing cameras help accurately identify traffic signs. They continuously capture real-time images of the road ahead so that the ADAS can detect and classify traffic signs using pattern recognition algorithms. Traffic Sign Recognition (TSR) systems rely on these cameras to read speed limits, stop signs and other regulatory signs.

Once recognised, the information is conveyed to the driver through the dashboard or heads-up display.

Forward collision detection

Forward-facing cameras in ADAS systems capture high-resolution visuals of the road ahead to assess the distance between vehicles. They help identify potential collisions, backed by algorithms that estimate relative speed and object trajectory. This enables safety features, including adaptive cruise control and automatic emergency braking. So, drivers can receive timely warnings to react early or slow the vehicle when necessary.

Real-time lane monitoring

Forward-facing cameras detect lane markings on the road, helping the vehicle stay centred within the lane. Hence, if the ADAS identifies an unintentional lane departure, it can trigger an alert or adjust the steering to bring the vehicle back on track. Lane detection systems are useful in highway driving, where maintaining lane position is critical for safety.

Real-time processing of the camera's visual input drives the system to respond quickly to any deviations from the lane, ensuring smoother driving.

Object/pedestrian detection and avoidance

Object detection systems analyse visual input from forward-facing cameras to identify vehicles, pedestrians, cyclists, and road obstacles. These cameras work in tandem with other sensors to form a multi-layered safety net. Therefore, the system can distinguish between moving and stationary objects and initiate corrective action like alerts, steering correction, or brake activation when a collision risk is identified.

In urban environments, improved image recognition also empowers embedded vision systems to respond intelligently to pedestrian movements as well.

Vehicle detection in blind spots

In many ADAS setups, forward-facing cameras assist in detecting vehicles that may enter the vehicle's blind spots. Blind-spot monitoring relies on a combination of side-facing and forward-facing cameras to maintain a constant awareness of surrounding traffic. By integrating the camera's visual feed with radar data, the vehicle can identify cars approaching from the rear or side and notify the driver of potential collisions.

It helps avoid accidents, particularly during lane changes or merging onto highways.

e-con Systems’ cameras for autonomous mobility

Since 2003, e-con Systems has been designing, developing, and manufacturing high-performance OEM cameras. We offer several cameras that can be integrated into ADAS and other autonomous mobility applications. They come with ideal features such as HDR, night vision, multi-camera synchronisation capabilities, IP67/IP69K rating, and multiple interfaces, including GMSL.

Camera solutions for mobility:

• STURDeCAM31
• STURDeCAM34
• STURDeCAM84
• STURDeCAM88
• RouteCAM_CU22

The company’s autonomous mobility vision expertise includes ISP tuning, optics integration, AI/ML development, mechanical design support, functional safety compliance with ISO standards, and more. It also has deep experience in NVIDIA, NXP, TI, Qualcomm, and FPGA platforms.

Browse the Camera Selector Page to view e-con Systems’ portfolio.