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IoT: A Business Perspective
Published in Rebecca Lee Hammons, Ronald J. Kovac, Fundamentals of Internet of Things for Non-Engineers, 2019
The development of autonomous vehicles is having a major impact on the transportation industry. Vehicles are now being equipped with computer-controlled technologies, through the use of cameras, GPS, and sensors, which can drive and control the vehicle without human intervention. The potential impact of this development is that someday we are expected to have transportation that is completely controlled by automation, not humans. The obvious advantage is that, with computer-driven vehicles, the likelihood of accidents is greatly reduced because human error due to inattentiveness, distraction, or tiredness will not be an issue. This concept will undoubtedly have a major impact on the trucking and the taxi cab industries. There would be no need to have downtime with autonomous trucks because the driver is unavailable to operate the vehicle. Likewise, in cities having autonomous vehicles that could be summoned from a smartphone and automatically paid for through that application, the need for the traditional taxi cab would be eliminated. Autonomous vehicles could immediately drop people off at their desired location and could go self-park, if they were owned by individuals. The potential of autonomous vehicles is incredible (Figure 15.6).
Automated vehicles
Published in Lawrence A. Klein, ITS Sensors and Architectures for Traffic Management and Connected Vehicles, 2017
Several expressions are commonly used to describe vehicles with self-driving features [2]. Among these is the term automated vehicle, which describes a vehicle that contains one or more driving functions designed to relieve the driver of a particular task. In the limit, an array of these automated driving functions will make autonomous vehicle operation possible. Autonomous vehicle refers to any vehicle equipped with technology capable of operating the vehicle without the active physical control or monitoring of a driver, whether or not the technology is engaged. Autonomous driving mode occurs when an autonomous vehicle is operating or driving in autonomous mode, that is, with the autonomous technology engaged. Cooperative or connected vehicles are those that have telematics to engage vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or infrastructure-to-vehicle (I2V) exchange of information that warns the driver of a potential crash or other unsafe driving condition, for example, curve ahead, slippery road surface, work zone, or pedestrian crossing.
Evolution Of Intelligent Transportation Systems
Published in Dušan Teodorović, The Routledge Handbook of Transportation, 2015
Several automobile manufacturers currently have an active autonomous vehicle program. Google’s self-driving car has received much attention lately due to its extensive testing in real-world traffic conditions. It has accumulated over 700,000 accident-free miles of combined highway and city street mileage (IEEE Spectrum, 2014) as of the writing of this article. Such developments have led some researchers to anticipate that the autonomous vehicles may become mainstream in 10 to 15 years (Luettel et al., 2012). The automation of vehicles is expected to occur in several phases or levels. NHTSA has defined five levels of vehicle automation ranging from Level 0 to Level 4 (NHTSA, 2014). Table 4 provides a summary of automation levels suggested by NHTSA.
Empirical study on consumer’s acceptance of delivery robots in France
Published in International Journal of Logistics Research and Applications, 2023
Ouail Oulmakki, Jerome Verny, Milena Janjevic, Marwa Khalfalli
The transportation and delivery industries are poised to undergo major transformations with the advent of autonomous vehicles. For example, demand for contactless delivery services during the COVID-19 pandemic has increased the use of autonomous delivery vehicles. In China, autonomous delivery vans and drones are used to deliver medicinal and food products, respectively. A European startup, Last Mile Autonomous Delivery (LMAD), launched an autonomous last-mile delivery platform in several European countries, such as the Aalto University campus in Finland and the Nokia Paris-Saclay campus in France. In the United States, startup Nuro obtained a license to commercially deploy its autonomous delivery vehicles in California, with its R2 model being used for deliveries in Houston. Overall, the COVID-19 pandemic accelerated the development of smart last-mile logistics.
Automatic traffic modelling for creating digital twins to facilitate autonomous vehicle development
Published in Connection Science, 2022
Shao-Hua Wang, Chia-Heng Tu, Jyh-Ching Juang
Autonomous driving software is the central control of an autonomous vehicle, and it receives the sensor data and reacts based on the perceived information. The autonomous driving software is considered as a platform that consolidates different algorithms to facilitate the self-driving researches, such as Zhao et al. (2021), Das and Chand (2021), Jiang et al. (2021), and Zhang et al. (2021). In addition to those proprietary solutions, Apollo (Baidu, 2020) and Autoware (Kato et al., 2015) are the open-source alternatives, both of which are highly integrated with the SVL simulator for autonomous vehicle simulations. In this work, we use the Robot Operating System (ROS) based driving software, Autoware, to facilitate the development of traffic modelling; further information is detailed in Section 4; note that ROS is a software framework to create robotic applications. In the real world, Autoware is running within the physical vehicle. On the contrary, in the simulated environment, Autoware can be run in either the co-simulation scheme or the standalone scheme.
Long-term prediction for high-resolution lane-changing data using temporal convolution network
Published in Transportmetrica B: Transport Dynamics, 2022
Yue Zhang, Yajie Zou, Jinjun Tang, Jian Liang
Autonomous vehicles can effectively mitigate traffic congestion, improve traffic safety and reduce vehicle emission pollution. One challenging task of autonomous driving is to model the driving behaviour and then predict the future vehicle movement. As one of the basic driving behaviours, lane change is a complex vehicle movement and can potentially result in serious traffic accidents. Lane change includes vehicle movement in both horizontal and vertical directions, and frequent lane change behaviours can significantly affect traffic flow operation. The national highway traffic safety administration estimates that in 2007, among all traffic accidents reported by the police, lane change and merging traffic accidents accounted for about 0.5% of traffic fatality (Guo, Wotring, and Antin 2010). In addition, studies have shown that dangerous lane changes can cause unstable traffic flow (Yang et al. 2009), and the lane change/merging accidents can also cause significant traffic delay (Chovan et al. 1994). Modelling the lane-changing process is useful for advanced driver assistance system (ADAS) (Nilsson et al. 2017) to implement automatic emergency braking, forward collision warning and lane departure warning, etc. Therefore, accurate prediction models are definitely useful to improve the safety of lane-changing behaviour.