Analyzable Parameters Dominated Vehicle Platoon Dynamics Modeling and Analysis: A Physics-Encoded Deep Learning Approach

Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon dynamics modeling plays a crucial role in predicting and optimizing the interactions between vehicles. Existing efforts lack the extraction and capture of vehicle behavior interaction features at the platoon scale. More importantly, maintaining high modeling accuracy without losing physical analyzability remains to be solved. To this end, this paper proposes a novel physics-encoded deep learning network, named PeMTFLN, to model the nonlinear vehicle platoon dynamics. Specifically, an analyzable parameters encoded computational graph (APeCG) is designed to guide the platoon to respond to the driving behavior of the lead vehicle while ensuring local stability. Besides, a multi-scale trajectory feature learning network (MTFLN) is constructed to capture platoon following patterns and infer the physical parameters required for APeCG from trajectory data. The human-driven vehicle trajectory datasets (HIGHSIM) were used to train the proposed PeMTFLN. The trajectories prediction experiments show that PeMTFLN exhibits superior compared to the baseline models in terms of predictive accuracy in speed and gap. The stability analysis result shows that the physical parameters in APeCG is able to reproduce the platoon stability in real-world condition. In simulation experiments, PeMTFLN performs low inference error in platoon trajectories generation. Moreover, PeMTFLN also accurately reproduces ground-truth safety statistics. The code of proposed PeMTFLN is open source.

Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization

Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, this paper presents distributed algorithms for control and optimization over vehicular networks. First, we formulate the autonomous vehicle platooning framework based on model-predictive-control (MPC) strategies and present its objective optimization as a cooperative quadratic cost function. Then, we propose a distributed algorithm to locally optimize this objective at every vehicle subject to data quantization over the communication network of vehicles. In contrast to most existing literature that assumes ideal communication channels, log-scale data quantization over the network is addressed in this work, which is more realistic and practical. In particular, we show by simulation that the proposed log-quantized algorithm reaches optimal convergence with less residual and optimality gap. This outperforms the existing literature considering uniform quantization which leads to a large optimality gap and residual.

Distributed Observer Design for Tracking Platoon of Connected and Autonomous Vehicles

Intelligent transportation systems (ITS) aim to advance innovative strategies relating to different modes of transport, traffic management, and autonomous vehicles. This paper studies the platoon of connected and autonomous vehicles (CAV) and proposes a distributed observer to track the state of the CAV dynamics. First, we model the CAV dynamics via an LTI interconnected system. Then, a consensus-based strategy is proposed to infer the state of the CAV dynamics based on local information exchange over the communication network of vehicles. A linear-matrix-inequality (LMI) technique is adopted for the block-diagonal observer gain design such that this gain is associated in a distributed way and locally to every vehicle. The distributed observer error dynamics is then shown to follow the structure of the Kronecker matrix product of the system dynamics and the adjacency matrix of the CAV network. The notions of survivable network design and redundant observer scheme are further discussed in the paper to address resilience to link and node failure. Finally, we verify our theoretical contributions via numerical simulations.

Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning

This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability inherent to platoon systems. The framework first develops a physics-based controller to model vehicle dynamics, using driving speed as input to optimize safety and efficiency. Then the residual controller, based on neural network (NN) learning, enriches the prior knowledge of the physical model and corrects residuals caused by vehicle dynamics. By integrating the physical model with data-driven online learning, the PERL framework retains the interpretability and transparency of physics-based models and enhances the adaptability and precision of data-driven learning, achieving significant improvements in computational efficiency and control accuracy in dynamic scenarios. Simulation and robot car platform tests demonstrate that PERL significantly outperforms pure physical and learning models, reducing average cumulative absolute position and speed errors by up to 58.5% and 40.1% (physical model) and 58.4% and 47.7% (NN model). The reduced-scale robot car platform tests further validate the adaptive PERL framework's superior accuracy and rapid convergence under dynamic disturbances, reducing position and speed cumulative errors by 72.73% and 99.05% (physical model) and 64.71% and 72.58% (NN model). PERL enhances platoon control performance through online parameter updates when external disturbances are detected. Results demonstrate the advanced framework's exceptional accuracy and rapid convergence capabilities, proving its effectiveness in maintaining platoon stability under diverse conditions.

Rigid Communication Topologies: Impact on Stability, Safety, Energy Consumption, Passenger Comfort, and Robustness of Vehicular Platoons

This paper investigates the impact of rigid communication topologies (RCTs) on the performance of vehicular platoons, aiming to identify beneficial features in RCTs that enhance vehicles behavior. We introduce four performance metrics, focusing on safety, energy consumption, passenger comfort, and robustness of vehicular platoons. The safety metric is based on momentary distances between neighboring vehicles, their relative velocities, and relative accelerations. Thus, to have access to these relative values, we formulate the coupled dynamics between pairs of neighboring vehicles, considering initial conditions (position, velocity, acceleration), leader vehicle's velocity/acceleration trajectory, deployed RCT, and vehicles' parity/disparity. By decoupling the dynamics using a mapping matrix structured on deployed RCT, vehicles' features, and control gains, precise formulations for distance errors, relative velocities, and relative accelerations between all neighboring vehicles, over the travel time, are obtained. Comparing performance metric results across RCTs highlights that downstream information transmission-from vehicles ahead, particularly the leader vehicle, to vehicles behind-significantly enhances platoon stability, safety, energy consumption, and passenger comfort metrics. Conversely, receiving state information from vehicles behind degrades metrics, compromising safety, increasing energy consumption, and reducing passenger comfort. These findings underscore that forward-looking, leader-centric communications between vehicles markedly enhance platoon efficiency and safety.

Mining Platoon Patterns from Traffic Videos

Discovering co-movement patterns from urban-scale video data sources has emerged as an attractive topic. This task aims to identify groups of objects that travel together along a common route, which offers effective support for government agencies in enhancing smart city management. However, the previous work has made a strong assumption on the accuracy of recovered trajectories from videos and their co-movement pattern definition requires the group of objects to appear across consecutive cameras along the common route. In practice, this often leads to missing patterns if a vehicle is not correctly identified from a certain camera due to object occlusion or vehicle mis-matching. To address this challenge, we propose a relaxed definition of co-movement patterns from video data, which removes the consecutiveness requirement in the common route and accommodates a certain number of missing captured cameras for objects within the group. Moreover, a novel enumeration framework called MaxGrowth is developed to efficiently retrieve the relaxed patterns. Unlike previous filter-and-refine frameworks comprising both candidate enumeration and subsequent candidate verification procedures, MaxGrowth incurs no verification cost for the candidate patterns. It treats the co-movement pattern as an equivalent sequence of clusters, enumerating candidates with increasing sequence length while avoiding the generation of any false positives. Additionally, we also propose two effective pruning rules to efficiently filter the non-maximal patterns. Extensive experiments are conducted to validate the efficiency of MaxGrowth and the quality of its generated co-movement patterns. Our MaxGrowth runs up to two orders of magnitude faster than the baseline algorithm. It also demonstrates high accuracy in real video dataset when the trajectory recovery algorithm is not perfect.

EH from V2X Communications: the Price of Uncertainty and the Impact of Platooning

In this paper, we explore how radio frequency energy from vehicular communications can be exploited by an energy harvesting device (EHD) placed alongside the road to deliver data packets through wireless connection to a remote Access Point. Based on updated local topology knowledge, we propose a cycle-based strategy to balance harvest and transmit phases at the EHD, in order to maximize the average throughput. A theoretical derivation is carried out to determine the optimal strategy parameters setting, and used to investigate the effectiveness of the proposed approach over different scenarios, taking into account the road traffic intensity, the EHD battery capacity, the transmit power and the data rate. Results show that regular traffic patterns, as those created by vehicles platooning, can increase the obtained throughput by more than 30% with respect to irregular ones with the same average intensity. Black out probability is also derived for the former scenario. The resulting tradeoff between higher average throughput and lower black out probability shows that the proposed approach can be adopted for different applications by properly tuning the strategy parameters.

Performance Analysis of Resource Allocation Algorithms for Vehicle Platoons over 5G eV2X Communication

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic exchange of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM exchanges in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET++, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon Information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-leader following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X platooning IFT selected, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of Information (AoI), and throughput.

Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is employed. The proposed approach leverages the semantic information to optimize the allocation of communication resources. By integrating a distributed multi-agent reinforcement learning (MARL) algorithm, SAMRAMARL enables autonomous decision-making for each vehicle, channel assignment optimization, power allocation, and semantic symbol length based on the contextual importance of the transmitted information. This semantic-awareness ensures that both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications prioritize data that is critical for maintaining safe and efficient platoon operations. The framework also introduces a tailored quality of experience (QoE) metric for semantic communication, aiming to maximize QoE in V2V links while improving the success rate of semantic information transmission (SRS). Extensive simulations has demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE and communication efficiency in C-V2X platooning scenarios.

Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles

Dynamic scheduling of access to shared resources by autonomous systems is a challenging problem, characterized as being NP-hard. The complexity of this task leads to a combinatorial explosion of possibilities in highly dynamic systems where arriving requests must be continuously scheduled subject to strong safety and time constraints. An example of such a system is an unsignalized intersection, where automated vehicles' access to potential conflict zones must be dynamically scheduled. In this paper, we apply Neural Monte Carlo Tree Search (NMCTS) to the challenging task of scheduling platoons of vehicles crossing unsignalized intersections. Crucially, we introduce a transformation model that maps successive sequences of potentially conflicting road-space reservation requests from platoons of vehicles into a series of board-game-like problems and use NMCTS to search for solutions representing optimal road-space allocation schedules in the context of past allocations. To optimize search, we incorporate a prioritized re-sampling method with parallel NMCTS (PNMCTS) to improve the quality of training data. To optimize training, a curriculum learning strategy is used to train the agent to schedule progressively more complex boards culminating in overlapping boards that represent busy intersections. In a busy single four-way unsignalized intersection simulation, PNMCTS solved 95\% of unseen scenarios, reducing crossing time by 43\% in light and 52\% in heavy traffic versus first-in, first-out control. In a 3x3 multi-intersection network, the proposed method maintained free-flow in light traffic when all intersections are under control of PNMCTS and outperformed state-of-the-art RL-based traffic-light controllers in average travel time by 74.5\% and total throughput by 16\% in heavy traffic.