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The deceleration lane isbefore numbers to minus signs. a critical part of the freeway, enabling vehicles to exit the expressway safely and in an orderly fashion. However, drivers are human and thus make subjective decisions while driving; as such, each driver may approach and traverse the deceleration lane differently. This variance, which can cause major traffic disruptions and collisions, can be observed – and even mitigated, as proposed herein – based on a driver's particular characteristics. To study the variances in the longitudinal vehicle positions and microscopic operating characteristics of different drivers on a freeway exit area, a field operational test involving 46 subjects was carried out to collect data on driver characteristics, vehicle motion postures, micro‐driving operations, and road geometric elements. The 46 participants were observed based on experience and gender, and the mathematical statistical method was used to analyse driving differences in the deceleration lane. The results show that (1) the vehicle motion state can be divided into four operational stages on the deceleration lane: the pre‐deceleration process, the dynamic adjustment process, the first braking process, and the second braking process; (2) drivers generally adopted deceleration behaviour rather than maintain uniform speed when driving in the taper; (3) about 50% of drivers braked after entering the deceleration lane; (4) male drivers and skilled drivers were more inclined to drive at a higher speed on the deceleration lane, and female drivers showed a sharp increase in braking frequency once they travelled 110–150 m downstream of the taper starting point. The results of this study provide data and insights for deceleration lane design, traffic management, and driver training. [ABSTRACT FROM AUTHOR]

In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) has important significance for the development of the connected and automated vehicle (CAV) industry. The traditional proportional integral derivative (PID) platoon controller adjustment is not only time-consuming and laborious, but also unable to adapt to different working conditions. This paper proposes a learning control method for a vehicle platooning system using a deep deterministic policy gradient (DDPG)-based PID. The main contribution of this study is automating the PID weight tuning process by formulating this objective as a deep reinforcement learning (DRL) problem. The longitudinal control of the vehicle platooning is divided into upper and lower control structures. The upper-level controller based on the DDPG algorithm can adjust the current PID controller parameters. Through offline training and learning in a SUMO simulation software environment, the PID controller can adapt to different road and vehicular platooning acceleration and deceleration conditions. The lower-level controller controls the gas/brake pedal to accurately track the desired acceleration and speed. Based on the hardware-in-the-loop (HIL) simulation platform, the results show that in terms of the maximum speed error, for the DDPG-based PID controller this is 0.02–0.08 m/s less than for the conventional PID controller, with a maximum reduction of 5.48%. In addition, the maximum distance error of the DDPG-based PID controller is 0.77 m, which is 14.44% less than that of the conventional PID controller. [ABSTRACT FROM AUTHOR]