An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

Abstract

The mass production of autonomous vehicle is coming, thanks to the rapid progress of autonomous driving technology, especially the recent breakthroughs in LiDAR sensors, GPUs, and deep learning. Many automotive and IT companies represented by Waymo and GM are constantly promoting their advanced autonomous vehicles to hit public roads as early as possible. This paper systematically reviews the latest development and future trend of the autonomous vehicle technologies, discusses the extensive application of AI in ICV, and identifies the key problems and core challenges facing the commercialization of autonomous vehicle. Based on the review, it forecasts the prospects and conditions of autonomous vehicle’s mass production and points out the arduous, long-term and systematic nature of its development.

Octree‐based point cloud simulation to assess the readiness of highway infrastructure for autonomous vehicles

“…Sensors' ranges and shortcomings are considered the major contributing factors to the effectiveness of CAS (Elliott et al., 2019; Seif & Hu, 2016). Using vehicle communications (V2X) and mapping and localization technologies, CAVs could receive external information on road conditions, obstacles, and road users beyond their sensor range (3M TM , 2020c; Elliott et al., 2019; Z. Liu, Jiang, Tan, & Zhao, 2020). Each component of CAVs technologies (e.g., different types of sensors, mapping, and navigation systems) has its own limitations, and no single technology is sufficient to solely support high levels of autonomous driving (3M TM , 2020a, 2020c; Elliott et al., 2019; Z. Liu et al., 2020; Seif & Hu, 2016).…”

“…Hence, the assessment of visibility conditions based on CAVs parameters at locations with limited ASDs is critical to the successful long‐term deployment of these technologies. The fusion of various technologies can support, to some extent, the redundancies of information sharing between CAVs and the roadway environment (3M TM , 2020c; Z. Liu et al., 2020). An example of redundancy is receiving an advisory speed from a traffic sign as well as a V2I communication.…”

“…Fusing more than one countermeasure with CAVs technologies should enhance the overall reliability of CAVs, thereby preventing or mitigating potential failures (3M TM , 2020c; Cui, Liew, Sabaliauskaite, & Zhou, 2019; Z. Liu et al., 2020). The range, type of V2I connection, and site topography must be taken into account when enhancing connectivity.…”

Otonom Araçların Akıllı Ulaşım Politikaları Üzerindeki Etkileri

“…Elektromanyetik dalgalar yardımı ile çalışır. Öncelikli kullanım amacı, araç çevresinde bulunan nesne tespiti, çarpışma önleme, park yardımı ve kör nokta gözetimidir (Liu et al, 2020).…”

“…LIDAR, bir çeşit entegre optik tarayıcıdır ve bu frekans bandını kullanarak işleyen bir sistemdir. Bu sistemin avantajı yol üzerine yerleştirilen yol işaretleri, trafik sinyalizayonu gibi nesnelerin yüksek çözünürlükte üç boyutlu imajını çıkarabilmesidir (Liu et al, 2020). Aynı zamanda ortam aydınlatması ile bağlı değildir.…”