scispace - formally typeset
Search or ask a question

What is best radar sensor for a smart traffic intersection system? 


Best insight from top research papers

The best radar sensor for a smart traffic intersection system is determined by evaluating the performance:cost ratio. A performance evaluation study compared the Delphi ESR radar sensor (ESR), Ibeo Lux laser sensor (LUX), and Smartmicro Umrr9 radar sensor (UMRR9) for a Cooperative Intersection Collision Avoidance System - Stop Sign Assist (CICAS-SSA) . The study aimed to determine which sensor provided the best performance:cost ratio as a mainline sensor for the system. The study analyzed the accuracy of position and speed measurements for each sensor by comparing them with DGPS measurements. Based on the evaluation, the sensor with the best performance:cost ratio for the CICAS-SSA mainline sensor was not specified in the abstract. Therefore, further investigation or access to the full paper is required to determine the best radar sensor for a smart traffic intersection system.

Answers from top 4 papers

More filters
Papers (4)Insight
The Delphi ESR radar sensor was found to have the best combination of performance and price for the CICAS-SSA mainline sensor.
The paper does not mention the specific radar sensor used for the smart traffic intersection system.
The paper does not specify the best radar sensor for a smart traffic intersection system.
The paper does not mention the specific radar sensor that is considered the best for a smart traffic intersection system.

Related Questions

What are the current advancements in using IoT for smart traffic monitoring?5 answersCurrent advancements in smart traffic monitoring involve innovative approaches utilizing IoT technologies. One such approach integrates IoT devices like infrared sensors for traffic density monitoring and signal control. Additionally, the use of Tinkercad, a 3D modeling tool, combined with cameras and sensors, enables real-time detection of traffic violations such as over-speeding and illegal parking. Moreover, IoT devices in the transport industry facilitate predictive maintenance for trains through the Internet of Things (IoT), cloud computing, and artificial intelligence, enhancing operational efficiency and safety measures. These advancements showcase the integration of IoT in traffic management systems, offering solutions for congestion reduction, violation detection, and predictive maintenance in the transportation sector.
What are the main types of sensors used in autonomous driving?5 answersThe main types of sensors used in autonomous driving include camera, LiDAR, GNSS/INS, and millimeter-wave radar. These sensors are essential for perceiving the road environment and ensuring the safety and reliability of autonomous vehicles. Cameras are used as perception sensors to scan the surrounding area and provide visual information for building an environmental model. LiDAR sensors use laser beams to measure distances and create a detailed 3D map of the surroundings. GNSS/INS sensors provide absolute position and time information, acting as a standard spatiotemporal reference for all autonomous systems. Millimeter-wave radar sensors are used for detecting objects and measuring their speed. These sensors work together to provide a comprehensive understanding of the vehicle's surroundings and enable autonomous driving capabilities.
Which RF sensor or IF sensor in oill spill detection is best?4 answersThe best sensor for oil spill detection depends on the specific requirements and conditions. One study proposed a novel microwave in situ oil spill sensor that detected oil spillage in seawater by measuring changes in reflection and transmission coefficients using ultra-wideband underwater microwave trefoil antennas. Another study evaluated the feasibility of using fluorometric sensors in flow-through systems for real-time oil spill detection, but noted that the presence of interference compounds affected the detection capabilities. A different study focused on the use of polarimetric synthetic aperture radar (SAR) for oil spill detection, demonstrating the superiority of a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) over traditional spectral similarity measures. Additionally, radar and passive microwave techniques were highlighted as effective options for wide area surveillance and slick thickness measurements. Therefore, the choice of RF or IF sensor for oil spill detection depends on the specific application and environmental conditions.
What are the most efficient sensor fusion algorithms used in autonomous vehicles?4 answersThe most efficient sensor fusion algorithms used in autonomous vehicles include middle fusion, which combines feature representations from different sensing modalities at intermediate layers. Another approach is the fusion of camera and LiDAR sensors, which combines the strengths of both sensors for better object detection and distance estimation. These algorithms aim to improve the accuracy and latency efficiency of perception and decision-making in autonomous vehicles, which are crucial for ensuring safety. A3Fusion is a software-hardware system that specializes in adaptive, agile, and aligned fusion, achieving high efficiency through an adaptive multi-modal learning network architecture and a latency-aware, agile network architecture optimization algorithm. Additionally, A3Fusion proposes a FPGA-based accelerator to reduce compute overheads.
What are the different types of sensors used in cars?5 answersThe different types of sensors used in cars include current sensors, polymer composite/nanocomposite sensors, and exteroceptive sensors. Current sensors are used to monitor the wide range of current and bandwidth of electrical devices in cars, and they are typically designed using Hall-effect open-loop sensors composed of cheap magnetic materials. Polymer composite/nanocomposite sensors are gaining attraction in the automotive industry due to advancements in functional polymers and fillers. These sensors offer efficient sensing capabilities while retaining mechanical flexibility, scalability, and robustness. Exteroceptive sensors play a critical role in Advanced Driving Assistance Systems (ADAS) and autonomous vehicles. They provide information about the external environment and are used for safety, navigation, and driving profiling purposes. Different technologies are used for exteroceptive sensors, and they are available in existing Off-the-Shelf (OTS) devices.
Could you use MPC with smart intersection traffic management system?3 answersModel Predictive Control (MPC) can be used in smart intersection traffic management systems. MPC is an effective tool for reducing traffic congestion and improving vehicle flow. It can be applied in both centralized and decentralized schemes. In the centralized scheme, a central unit controls the entire network, providing an optimal solution but may not meet real-time computation requirements for large networks. In the decentralized scheme, each intersection has its own control unit, which sends local information to an aggregator. The aggregator receives information from all control units and the emergency vehicle, augments it, and shares it with the control units. This scheme is suitable for large networks and can provide a suboptimal solution. The proposed decentralized MPC strategy considers safety constraints and pedestrian requests, and can be easily scaled up to larger networks while maintaining comparable performance with centralized methods.