An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems

• Main Authors: Fourie, C.M (Author), Myburgh, H.C (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: ADAS; ADAS and smartphones; Advanced driver assistance systems; Advanced driver-assistance system(s); Advanced driver-assistance system(s) and smartphone; Automobile drivers; Cameras; Costs; Intra vehicles; IVWSN; Low-costs; Multimedia sensor networks; object detection; Object detection; Object recognition; Smart phones; Smartphones; Vehicles; Vehicular wireless; Wi-Fi; Wireless local area networks (WLAN); Wireless multimedia; Wireless sensor networks; WMSN
• Online Access: View Fulltext in Publisher

 Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.