# A Deep Reinforcement Learning Approach for Efficient, Safe and Conformtable Driving +
###### Abstract  
Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the vehicle's environmental context. We propose a data-centric application of Adaptive Cruise Control employing Deep Reinforcement Learning (DRL). Our DRL approach considers multiple objectives, including safety, passengers' comfort, and efficient road capacity usage. We compare the proposed framework's performance to traditional ACC approaches by incorporating such schemes into the CoMoVe framework, which realistically models communication, traffic, and vehicle dynamics. Our solution offers excellent performance concerning stability, comfort, and efficient traffic flow in diverse real-world driving conditions. Notably, our DRL scheme can meet the desired values of road usage efficiency most of the time during the lead vehicle's speed-variation phases, with less than 40% surpassing the desirable headway. In contrast, its alternatives increase headway during such transient phases, exceeding the desired range 85% of the time, thus degrading performance by over 300% and potentially contributing to traffic instability. Furthermore, our results emphasize the importance of vehicle connectivity in collecting more data to enhance the ACC's performance. c onteched vehicles; vehicle dynamics; adaptive cruise control; traffic simulation; machine learning +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. Adaptive Cruise Control (ACC) is emerging as one of the most popular and relevant applications of ADAS that can benefit from ML techniques. Indeed, especially in adverse road conditions, standard ACC increases the mental and temporal workload of the drivers [2], often leading to human errors. On the other hand, connected autonomous vehicles are required to implement sophisticated control of the vehicle's behavior in terms of efficiency, safety, and comfort under all road conditions. The headway control between two consecutive vehicles can increase road capacity and stabilize traffic flow [3; 4; 5], thereby improving traffic efficiency. The safety term addresses issues pertaining to vehicle stability and the Time-To-Collision between two vehicles. The comfort term regulates the time derivative of the vehicle's acceleration to provide passengers with a jerk-free travel experience. In particular, we are focusing on improving the safety of vehicles by accounting for vehicle stability in different road conditions, an aspect that has been overlooked in the literature. Generally, the literature on ACC [6; 7] assesses the safety of a vehicle only based on the inter-vehicular distance between the ego vehicle and the lead vehicle. However, road conditions, such as wet or icy surfaces, can impact vehicle stability, affecting passenger safety. Therefore, in our study, we have incorporated longitudinal slip as a vehicle stability indicator to ensure the safety of the passengers, a factor other related works have failed to tackle. The proposed methodology relies on the longitudinal wheel slip and tire-road friction coefficient to prevent loss of traction with the road surface under diverse conditions, effectively addressing the drawback. However, the accurate representation of the vehicle states is difficult in real-world conditions, for instance, GNSS positioning errors. Even though we assumed the availability of such information from simulation models, several research works [8; 9; 10] have been proposed to estimate them in real-life situations. In this work, we address the challenges posed by the ACC in connected autonomous vehicles by utilizing Reinforcement Learning (RL), a particular ML technique, to address the main problems with the standard ACC system.