Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions
On the map: End-to-end autonomous driving → Cooperative and V2X driving
Abstract (arXiv)
Autonomous Vehicle (AV) technology is advancing rapidly, promising a significant shift in road transportation safety and potentially resolving various complex transportation issues. With the increasing deployment of AVs by various companies, questions emerge about how AVs interact with each other and with human drivers, especially when AVs are prevalent on the roads. Ensuring cooperative interaction between AVs and between AVs and human drivers is critical, though there are concerns about possible negative competitive behaviors. This paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs, incorporating sharing and caring V2V communication strategy to enhance mutual coordination. A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study, which focuses on how the human drivers will react to the AV driving alongside them on the same road. Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance. The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs. The survey shows that safety in mixed traffic cannot be guaranteed, as we cannot control human ego-driven actions if they decide to compete with AV. Consequently, this paper advocates for enhanced research into the safe incorporation of AVs on public roads.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 8 of its 22 references that are in the map. First posted on arXiv 8 Dec 2023.
Builds on
8 of 22 references.
References · 8
- 2018A Survey on Deep Transfer Learningcited by 419
- 2018Unity: A General Platform for Intelligent Agentscited by 236
- 2021Embodied Intelligence via Learning and Evolutioncited by 163
- 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 110
- 2020Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learningcited by 65
- 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 40
- 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 31
- 2020High-speed Autonomous Drifting with Deep Reinforcement Learningcited by 24
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
- 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
- 2023Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness
- 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
- 2020A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
- 2021Social Coordination and Altruism in Autonomous Driving
- 2025Self-Interest and Systemic Benefits: Emergence of Collective Rationality in Mixed Autonomy Traffic Through Deep Reinforcement Learning
- 2026Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehicles