## 5 Conclusions and Future Research  
In this work, two longitudinal navigational strategies were developed for implementing RBS on EAD while navigating signalized intersections with a traffic light. In the virtual environment, internal data are acquired through odometry and external data through SPaT coming from V2X communications. The data are fed into a perception module that provides location information to a CEV controller. The controller implements a lateral pure pursuit algorithm. A rule- or FIS-based longitudinal controller determines the setpoint for an electric motor that outputs a torque that is implemented by the powertrain. Said torque is then transformed into positive or negative throttle. Finally, it is applied into a dynamic model of an ego vehicle. This model provides a realistic representation of the plant. The proposed algorithms implement a longitudinal navigation strategy that account for speed limit compliance, comfort, and power efficiency and regeneration. The numerical results enable the possibility of implementation of both systems into a physical system for controlled tests.  
Navigation of CEV at urban intersections with power management and restoration is a field with open research opportunities. After simulation, the numerical results show that the proposed systems can be employed as a benchmark for future researchers to validate new systems in terms of SPaT data, longitudinal velocity and acceleration, comfort, time to completion, success rate, consumed and regenerated power, and motoring and regeneration efficiency. Therefore, the open research questions along these lines are:  
1. Multiple intersection environments to alter the state of charge of a battery during sustained navigation.
2. Background vehicles to increase the complexity of the problem and help better understand the safety, comfort, and power management challenges.
3. The use of machine learning, artificial intelligence, or predictive control techniques that use the proposed longitudinal navigation systems as a benchmark and design rationale.  
Enhancing vehicles with visual and inertial sensors to cover non-SPaT areas. Sensor fusion techniques are required to tackle data availability, thereby expanding the problem into a partially observable Markov decision process.
* The integration of the proposed approach within more complex energy management and control strategies, where multiple vehicle functionalities and behaviors coexist.
* Safe and eco-approach and departure navigation in highly dynamic situations, such as lane changing, merging, overtaking, or obstacle avoidance maneuvers.  
Conceptualization, R.B.-M., R.G. and X.D.; methodology, R.B.-M., R.G., Z.M., Y.F., R.B.-B. and X.D.; software, R.B.-M.; validation, R.B.-M.; formal analysis, R.B.-M. and R.G.; investigation, [PERSON]-M., R.G. and X.D.; resources, X.D.; data curation, [PERSON]-M.; writing--original draft preparation, [PERSON]-M.; writing--review and editing, [PERSON]-M., R.G., Z.M., Y.F., [PERSON]-B. and X.D.; visualization, [PERSON]-M. and R.G.; supervision, X.D.; project administration, R.B.-B. and X.D.; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
[PERSON], and [PERSON] are supported by CIMB and the Data Science Hub at Tecnologico de Monterrey, and the AWS Cloud Credit for Research. Bautista-Montesano is funded by Tecnologico de Monterrey--Grant No. A00996397, Consejo Nacional de Ciencia y Tecnologia (CONACYT) under the scholarship 679120. Di is supported by NSF CPS-2038984.  
The authors declare no conflict of interest.