## 1 Introduction  
Currently, climate change and pollution are problems that are being addressed by the United Nations Sustainable Development Goals. These global issues directly affect transportation systems [1]. The goal of achieving net-zero emissions has led to the implementation of stricter government policies for vehicle manufacturers and has paved the way for vehicles powered through alternative energy sources. The automotive sector has invested more money than any other sector in research and development (R&D) in the clean energy innovation sector [2]. Longitudinal control has seen the development of various state-of-the-art algorithms, such as physics-informed deep learning [3] and causal discovery [4, 5], with a greater focus on energy efficiency in light of the growing use of EVs. Electric vehicles (EVs) are currently the subject of new designs, battery research, and energy-efficient automated navigation strategies [6]. Longitudinal energy-efficient navigation strategies that extend the driving range for EVs with partial or incomplete information from its environment are currently open research opportunities. Automated vehicles (AVs) have been introduced to the navigation environment during the past two decades [7]. Sensors provide internal data of the vehicle and the environment. They can be classified into internal (proprioceptive) and external (exteroceptive) state sensors [8]. Internal sensors measure values of the dynamic system such as linear and angular speeds and accelerations, forces, and wheel loads. Inertial measurement units (IMU), encoders, and global navigation satellite system (GNSS) are the most common proprioceptive devices. External sensors retrieve data from the environment, such as distance to the surrounding elements, or light intensity, e.g., cameras, radars, LiDARs, and ultrasonic sensors [9]. Sensor data are then processed to compute the internal states of the vehicle and to perceive its surroundings. Such perception data require filtration to ensure a reliable performance for low- and high-level systems [10]. Kalman filtering and its variations are the most common techniques used for data fusion to estimate ego states and the behavior of external elements [11]. The motion of AVs is controlled by a navigation system that should select suitable lateral and longitudinal actions. High dynamic environments represent a challenge for this technology [12]. Connected vehicles (CVs) are considered as a key element for the constantly evolving autonomous driving and cooperative driving automation fields [13]. They can potentially increase safety and traffic efficiency by sharing information with surrounding vehicles and infrastructure [14] with the goal of implementing a collaborative strategy to optimize traffic. Centralized and decentralized architectures have been proposed to test their performance in terms of communication, intersection control, and vehicle speed management [15]. An enhanced traffic strategy would limit the use of throttle and braking. Consequently, fuel usage, reduced emissions, and energy regeneration can be exploited during navigation [16]. However, state estimation and macroscopic vehicle control is an open research area to ensure safe ego navigation that does not completely rely on communication data. Eco-approach and departure (EAD) is a longitudinal guidance strategy for CVs navigating an urban signalized or unsignalized [17] intersection environment [18]. The main goal of EAD is to optimize energy, travel time, and comfort by controlling the speed profile of AVs. EAD strategies can face four scenarios in signalized intersections. The cruise control (CC) scenario traverses the intersection at a constant speed. Speed-up denotes when the vehicle increases its speed to cross the road junction. Coast-down with a full stop decreases the AV speed until it halts at the intersection during a red light. Then, it continues moving. Lastly, glide gradually slows down and crosses the intersection without completely stopping [19].