TRAFFIC SIGN RECOGNITION SYSTEM MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2023 - 2030)

Traffic Sign Recognition System Market Size and Trends

The Global Traffic Sign Recognition System Market size was valued at US$ 33.62 billion in 2023 and is expected to reach US$ 46.27 billion by 2030, growing at a compound annual growth rate (CAGR) of 4.7% from 2023 to 2030.

Traffic Sign Recognition System Market Drivers:

• Stringent Road Safety Regulations: Government regulations regarding road safety have been a major driver for the traffic sign recognition system market. Regulatory authorities across regions have implemented mandates for equipping vehicles with active safety systems. For instance, the New Car Assessment Program tests vehicles based on crash avoidance technologies including traffic sign recognition. In January 2023, European car makers will be subject to a revised scoring system that incorporates Driver Monitoring System (DMS) features to enhance Safety Assist evaluations. These mandatory regulations have widespread implications for both automotive manufacturers and Original Equipment Manufacturers (OEMs) globally. Notably, China has made significant strides in implementing and advancing these regulations since 2020.
• Increasing Integration of ADAS Features: The integration of advanced driver assistance systems (ADAS) including traffic sign recognition in mass market vehicles has catalyzed market growth. ADAS features such as automatic emergency braking, lane departure warning, and others are being integrated with traffic sign recognition to enhance their capabilities. According to estimates, the global ADAS market is expected to reach USD 74.57 billion by 2030. OEMs are offering ADAS bundled packages with traffic sign recognition and lane departure warning to tap into automotive safety demand.
• Technological Advancements in Computer Vision: Significant advances in computer vision, artificial intelligence, and machine learning have enhanced the capabilities of traffic sign recognition systems. Computer vision algorithms such as you only look once (YOLO), Single Shot Detector (SSD), and Faster Convolutional Neural Network (R-CNN) have enabled precise detection and recognition of road signage. Companies are using AI technologies such as deep learning and neural networks to improve detection accuracy under difficult conditions. The adoption of GPUs and dedicated AI accelerators in autonomous vehicles will further augment the precision of traffic sign recognition.
• Development of Connected Infrastructure: The development of smart city infrastructure and vehicle-to-everything (V2X) connectivity has created opportunities for traffic sign recognition systems. V2X allows real-time communication of traffic data from road infrastructure to vehicles which enhances dynamic signage detection. Moreover, High-Definition (HD) mapping data generated from connected infrastructure aids real-time localization and mapping. Companies are partnering with cities to collect infrastructure data for traffic sign inventories and digital maps.

Traffic Sign Recognition System Market Trends:

• Cloud-based Databases of Traffic Signs: The aggregation of traffic sign data into cloud-based databases for ADAS applications is a key trend. Companies are leveraging connected vehicle fleets and infrastructure mapping to build repositories covering road assets across cities. For instance, in March 2023, throughout the Navy Yard, Rekor's intelligent devices utilize artificial intelligence (AI), machine learning (ML), and enhanced edge processing to gather de-identified roadway data and conduct analysis. This data is then converted into essential traffic insights, which can be instantly viewed on Rekor's cloud-based platform. The analytics cover aspects such as visitor statistics, traffic volume, vehicle types, and the influence of both visitors and vehicles.
• Use of AI for Detection Accuracy: Artificial intelligence and deep learning integration for traffic sign recognition will gain traction owing to benefits such as near real-time object recognition, reduced processing time and accuracy across varying light conditions. Companies are utilizing neural networks, convolutional networks to accurately recognize obscured or partially visible signs. AI training using synthetic datasets is also driving algorithmic improvements for traffic sign recognition systems.
• Over the Air Updates: Automotive OEMs are leveraging over-the-air software updates to continuously train the algorithms and enhance the performance of ADAS systems including traffic sign recognition. OTA (Over-the-Air Update) updates allow new capabilities to be added without hardware upgrades or dealership visits. Companies are performing data aggregation and running simulations to refine computer vision models to minimize false positives in traffic sign recognition systems.
• Sensor Fusion for Reliability: The adoption of sensor fusion techniques combining camera, radar, and LiDAR will gain prominence to improve reliability and capabilities of traffic sign recognition systems. Sensor fusion provides redundancy in case of single sensor failure and enables functions such as distance estimation for dynamic speed limit adjustment. Companies are utilizing sensor fusion to accurately recognize signage in complex lighting conditions and occlusions.