Complementary Advantages of Multi-Sensors: The Right Time for Autonomous Vehicle Development

Since 2016, autonomous driving technology has gradually moved from a sci-fi concept to reality. Early discussions by companies like TI emphasized the importance of sensor and processor integration. Today, China is among the most active markets for autonomous driving. By the end of 2025, the first L3 conditional autonomous vehicles received approval, marking a shift from testing to commercial acceleration. 2026 is expected to be a key year for L3 mass production, with multi-sensor fusion becoming mainstream to enable safer and more reliable perception.

Nexisense, as a domestic intelligent sensor brand, specializes in multi-modal fusion technology, offering high-precision environmental perception solutions that support real-time decision-making in complex scenarios. This article analyzes the complementary advantages of multi-sensors and forecasts autonomous vehicle development trends.

Core Perception in Autonomous Driving: Why Multi-Sensors Are Needed

Autonomous vehicles “see” the world through a diverse sensor array. A single sensor cannot handle all scenarios: cameras excel in semantic recognition but are affected by lighting and weather; radar provides all-weather distance and speed measurements but has low resolution; LiDAR offers precise 3D point clouds but is sensitive to rain and fog.

Multi-sensor fusion compensates weaknesses, achieving redundancy and enhancement. For example, a camera + radar combination can reliably detect vehicles in foggy conditions, while LiDAR provides precise distance and shape information. This approach is now industry standard, with companies like Waymo deploying 5+ LiDARs, 6+ radars, and 29+ cameras for full coverage.

Fusion algorithms, such as BEV bird’s-eye view fusion or cross-modal attention mechanisms, integrate multi-source data into a unified environment model, improving object detection accuracy. By 2025–2026, 4D millimeter-wave radar is emerging, enhancing vertical perception with expected penetration exceeding 40%.

Advantages of Multi-Sensor Fusion

Accuracy and Reliability Improvement

Fusion significantly enhances system robustness. Cameras provide rich texture and color information, LiDAR contributes geometric structure, and radar ensures speed and distance measurements under adverse weather. Research shows multi-modal fusion can reduce object detection error rates below 5%, far superior to a single modality.

At complex urban intersections, fusion can simultaneously identify pedestrians, bicycles, traffic lights, and dynamic vehicles, avoiding blind spots of individual sensors.

All-Weather and Full-Scenario Adaptation

Passive sensors (visible/infrared cameras) are affected by lighting and weather, while active sensors (radar, LiDAR) are not limited by time or season. Combined use achieves comprehensive perception: radar dominates in fog and rain, while cameras + LiDAR provide detailed perception in clear conditions.

China's complex traffic environment (congestion, construction zones) requires this redundancy. Nexisense fusion modules have been tested for all-weather stability.

Cost and Performance Balance

Early LiDARs were expensive; today, solid-state LiDAR costs have decreased significantly, with rapid market growth in 2025. Fusion allows optimized configurations: L3 vehicles can reduce high-end LiDAR count, while L4/L5 require higher redundancy. Future AI-enhanced fusion algorithms will further reduce reliance on a single high-end sensor.

China Autonomous Driving: Latest Progress and Opportunities

In 2025, China implemented intensive autonomous driving policies: the Ministry of Industry approved the first L3 vehicles, including BYD, NIO, and Changan. Baidu Apollo and Pony.ai already operate full Robotaxi services in multiple cities, while WeRide expands overseas.

At the sensor level, domestic LiDAR (e.g., Hesai, RoboSense) has over 70% market share, and 4D millimeter-wave radar is rapidly adopted. Combining vehicle connectivity and onboard intelligence creates a “God’s-eye view,” reducing blind spot accidents.

Nexisense NS series supports camera + radar + LiDAR fusion, compatible with Modbus, CAN, and suitable for L3+ scenarios. Low-power design and high-precision algorithms help OEMs reduce costs and enhance safety.

Sensor Fusion Technology Trends

From 2026 to 2030, multi-modal fusion will evolve to end-to-end models, with lightweight high-precision maps + real-time perception becoming mainstream. Solid-state LiDAR and AI fusion chips (e.g., Ambarella CV3) reduce costs and expand service coverage.

China’s market potential is immense: by 2029, intelligent connected vehicles will account for 23% of global sales. Nexisense will continue optimizing fusion solutions for Robotaxi and autonomous delivery deployments.

FAQ

Why is multi-sensor fusion better than a single sensor? Fusion leverages complementary strengths, providing higher accuracy, reliability, and all-weather adaptability, significantly reducing false detections.

When will L3 autonomous driving scale in China? 2026 may be a key year, with policy and technology maturity accelerating pilot-to-mass production transition.

How do Nexisense sensors support autonomous vehicles? We provide multi-modal fusion modules with high precision and low power, supporting complex environment perception and real-time decision-making.

Will the number of sensors decrease in the future? Configurations will be optimized; companies like Waymo/Mobileye have reduced some sensors, but redundancy remains critical for safety.

Smart Logistics: Explosive Growth Driving Sensor Opportunities

Meta Description: In 2026, China’s smart logistics market exceeds 200 billion RMB, creating billion-level opportunities for sensors. Nexisense intelligent sensors support AGV navigation, RFID tracking, and warehouse automation, driving unmanned logistics and efficiency revolution.

Keywords: smart logistics sensors, sensor opportunities, AGV sensors, RFID logistics, warehouse automation, IoT logistics, Nexisense sensors, logistics digitalization.

In 2016, the Sensor China exhibition foresaw that Industry 4.0 and the e-commerce boom would propel smart logistics. By 2026, this prediction is reality: China’s smart logistics market exceeds 200 billion RMB, with a CAGR above 15%. Automated warehouses, AGVs, and robot sorting lines are now standard, supported by sensors as the "digital brain" from traditional signal converters to edge-intelligent, cloud-connected devices.

Nexisense, a domestic smart sensor player, participates deeply with high-precision, multi-modal fusion solutions, enhancing operational efficiency and reducing costs.

Sensors: The Digital Neural Network of Smart Logistics

Smart logistics relies on real-time data. Every step—from inbound, shelving, picking, outbound, to delivery—requires centimeter-level precision, starting with sensors:

• Photoelectric/light curtain sensors detect goods positions and provide safety protection.

• RFID enables contactless batch identification and full traceability.

• LiDAR + ultrasonic + vision sensors guide AGVs with high-precision navigation.

• Temperature, humidity, and pressure sensors ensure cold chain integrity and cargo safety.

These sensors work together, forming the digital perception layer of logistics.

Market Surge: Exponential Sensor Opportunities

Historical data: 2001 automation logistics market<20b 2014="" 2020="" 42.5b="">100B RMB, and by 2026 exceeds 200B RMB. Globally, logistics-related sensor demand continues rising, contributing 15–20% of new market by 2030.

E-commerce, labor shortages, and green pressures drive flexible and unmanned logistics. Each automation upgrade triggers sensor quantity and performance expansion.

Nexisense NS series sensors are designed for these dynamic environments: low power, IP67 protection, multi-protocol compatibility (Modbus, LoRa, IO-Link), deployed in top logistics enterprises, improving single warehouse throughput by 30%+.

Sensor Intelligence Revolution: From “Collection” to “Value Creation”

Modern sensors go beyond traditional roles, acting as IoT data hubs. Edge computing, multi-modal fusion, cloud interaction, OTA upgrades turn sensors from passive eyes into active brains.

RFID exemplifies this: contactless, batch read, real-time tracking. Nexisense invests in RFID + vision fusion modules, enabling edge AI for preliminary classification and anomaly detection, reducing cloud load and providing valuable data for inventory forecasting and path optimization.

Seizing Smart Logistics Sensor Opportunities

• System-level solutions (hardware + software + services)

• Open compatibility (supporting emerging standards like M2.COM)

• Intelligence level (edge AI + data value extraction)

• Balance of cost and reliability

Nexisense accelerates along this path, providing full perception layer solutions to help logistics enterprises quickly build intelligent networks.

FAQ

Which sensors are most needed now? High-precision LiDAR/vision navigation, reliable RFID readers, safety light curtains, collision and environment sensors.

Why is 2026 the best deployment window? Policy, mature technology, and market demand converge; early movers gain supply chain advantages.

How do Nexisense projects perform? Deployed in major e-commerce/manufacturing warehouses, improving efficiency 25–35% with high system stability and positive feedback.

Conclusion and Outlook

From foresight in 2016 to the full explosion in 2026, smart logistics is the most certain and robust growth track for sensors. It drives the industry from “component supplier” to “value creator.” Nexisense continues domestic innovation with reliable, easy-to-integrate, cost-controlled solutions, helping Chinese logistics enterprises lead in global supply chain intelligence. Opportunities are in front, and action shapes the future.