arXiv subjects

Panagiotis Angeloudis

Publications and source records attributed to Panagiotis Angeloudis.

At least 19 recordsLinked to original sources

arXiv2026

Are eHMIs always helpful? Investigating how eHMIs interfere with pedestrian behavior on multi-lane streets: An eye-tracking virtual reality experiment

Appropriate communication is crucial for efficient and safe interactions between pedestrians and autonomous vehicles (AVs). External human-machine interfaces (eHMIs) on AVs, which can be categorized as allocentric or egocentric, are considered a promising solution. While the effectiveness of eHMIs has been extensively studied, in complex environments, such as unsignalized multi-lane streets, their potential to interfere with pedestrian crossing behavior remains underexplored. Hence, a virtual reality-based experiment was conducted to examine how different types of eHMIs displayed on AVs affect the crossing behavior of pedestrians in multi-lane streets environments, with a focus on the gaze patterns of pedestrians during crossing. The results revealed that the presence of eHMIs significantly influenced the cognitive load on pedestrians and increased the possibility of distraction, even misleading pedestrians in cases involving multiple AVs on multi-lane streets. Notably, allocentric eHMIs induced higher cognitive loads and greater distraction in pedestrians than egocentric eHMIs. This was primarily evidenced by longer gaze time and higher proportions of attention for the eHMI on the interacting vehicle, as well as a broader distribution of gaze toward vehicles in the non-interacting lane. However, misleading behavior was mainly triggered by eHMI signals from yielding vehicles in the non-interacting lane. Under such asymmetric signal configurations, egocentric eHMIs resulted in a higher misjudgment rate than allocentric eHMIs. These findings highlight the importance of enhancing eHMI designs to balance the clarity and consistency of the displayed information across different perspectives, especially in complex multi-lane traffic scenarios. This study provides valuable insights regarding the application and standardization of future eHMI systems for AVs.

cs.HC↗

arXiv2025

Strategic Fusion of Vision Language Models: Shapley-Credited Context-Aware Dawid-Skene for Multi-Label Tasks in Autonomous Driving

Large vision-language models (VLMs) are increasingly used in autonomous-vehicle (AV) stacks, but hallucination limits their reliability in safety-critical pipelines. We present Shapley-credited Context-Aware Dawid-Skene with Agreement, a game-theoretic fusion method for multi-label understanding of ego-view dashcam video. It learns per-model, per-label, context-conditioned reliabilities from labelled history and, at inference, converts each model's report into an agreement-guardrailed log-likelihood ratio that is combined with a contextual prior and a public reputation state updated via Shapley-based team credit. The result is calibrated, thresholdable posteriors that (i) amplify agreement among reliable models, (ii) preserve uniquely correct single-model signals, and (iii) adapt to drift. To specialise general VLMs, we curate 1,000 real-world dashcam clips with structured annotations (scene description, manoeuvre recommendation, rationale) via an automatic pipeline that fuses HDD ground truth, vehicle kinematics, and YOLOv11 + BoT-SORT tracking, guided by a three-step chain-of-thought prompt; three heterogeneous VLMs are then fine-tuned with LoRA. We evaluate with Hamming distance, Micro-Macro-F1, and average per-video latency. Empirically, the proposed method achieves a 23% reduction in Hamming distance, 55% improvement in Macro-F1, and 47% improvement in Micro-F1 when comparing with the best single model, supporting VLM fusion as a calibrated, interpretable, and robust decision-support component for AV pipelines.

cs.CV↗

arXiv2025

ORThought: Benchmarking and Automating Logistics Optimization Modeling

Optimization modeling stands as the engine of scientific decision-making in logistics and transportation, yet its adoption is hindered by a steep expertise threshold and the latency of manual workflows. Automating this process via Large Language Models (LLMs) offers a potential solution, but current approaches face critical bottlenecks: (i) a lack of high-quality, complex benchmarks; (ii) methodological inefficiencies in autonomous multi-agent frameworks, which often exhibit instability and redundant computation; and (iii) evaluations that lack diagnostic depth. In this work, we address these challenges from the following three aspects. First, we introduce LogiOR, a diverse logistics benchmark with rigorous annotations, and enrich existing datasets with the same annotation standard to support community utilization. Second, we propose ORThought, a structured dual-agent framework. By incorporating expert-level modeling principles via chain-of-thought reasoning, ORThought eliminates the redundancy of uncontrolled autonomous agents. Third, extensive empirical evaluations demonstrate that ORThought consistently outperforms state-of-the-art baselines by 9-17 percentage points, exhibiting distinct advantages in handling complex constraints while maintaining high token efficiency. Building on these results, we further conduct a multidimensional error analysis, which identifies key failure modes and success factors, providing actionable insights for future research. The dataset and code are available at https://huggingface.co/datasets/LabMem012/LogiOR and https://github.com/ZJU-TSELab/ORThought, respectively.

cs.AI↗

arXiv2025

The Impact of Building-Induced Visibility Restrictions on Intersection Accidents

Traffic accidents, especially at intersections, are a major road safety concern. Previous research has extensively studied intersection-related accidents, but the effect of building-induced visibility restrictions at intersections on accident rates has been under-explored, particularly in urban contexts. Using OpenStreetMap data, the UK's geographic and accident datasets, and the UK Traffic Count Dataset, we formulated a novel approach to estimate accident risk at intersections. This method factors in the area visible to drivers, accounting for views blocked by buildings - a distinctive aspect in traffic accident analysis. Our findings reveal a notable correlation between the road visible percentage and accident frequency. In the model, the coefficient for "road visible percentage" is 1.7450, implying a strong positive relationship. Incorporating this visibility factor enhances the model's explanatory power, with increased R-square values and reduced AIC and BIC, indicating a better data fit. This study underscores the essential role of architectural layouts in road safety and suggests that urban planning strategies should consider building-induced visibility restrictions. Such consideration could be an effective approach to mitigate accident rates at intersections. This research opens up new avenues for innovative, data-driven urban planning and traffic management strategies, highlighting the importance of visibility enhancements for safer roads.

cs.CY↗

arXiv2024

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promising approach to address this issue is through the use of Vehicle-to-Vehicle (V2V) networks that allow for the sharing of perception information with nearby agents, preventing catastrophic accidents. In this article, we propose a collaborative control method based on a V2V network for sharing compressed LiDAR features and employing Proximal Policy Optimisation to train safe and efficient navigation policies. Unlike previous approaches that rely on expert data (behaviour cloning), our proposed approach learns the multi-agent policies directly from experience in the occluded environment, while effectively meeting bandwidth limitations. The proposed method first prepossesses LiDAR point cloud data to obtain meaningful features through a convolutional neural network and then shares them with nearby CAVs to alert for potentially dangerous situations. To evaluate the proposed method, we developed an occluded intersection gym environment based on the CARLA autonomous driving simulator, allowing real-time data sharing among agents. Our experimental results demonstrate the consistent superiority of our collaborative control method over an independent reinforcement learning method and a cooperative early fusion method.

cs.RO↗

arXiv2024

Towards a Universal Evaluation Model for Careful and Competent Autonomous Driving

Virtual scenario-based testing methods to validate autonomous driving systems are predominantly centred around collision avoidance, and lack a comprehensive approach to evaluate optimal driving behaviour holistically. Furthermore, current validation approaches do not align with authorisation and monitoring requirements put forth by regulatory bodies. We address these validation gaps by outlining a universal evaluation framework that: incorporates the notion of careful and competent driving, unifies behavioural competencies and evaluation criteria, and is amenable at a scenario-specific and aggregate behaviour level. This framework can be leveraged to evaluate optimal driving in scenario-based testing, and for post-deployment monitoring to ensure continual compliance with regulation and safety standards.

cs.RO↗

arXiv2024

Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation

This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle (AV) training and testing. CRITICAL stands out for its ability to generate diverse scenarios, focusing on critical driving situations that target specific learning and performance gaps identified in the Reinforcement Learning (RL) agent. The framework achieves this by integrating real-world traffic dynamics, driving behavior analysis, surrogate safety measures, and an optional Large Language Model (LLM) component. It is proven that the establishment of a closed feedback loop between the data generation pipeline and the training process can enhance the learning rate during training, elevate overall system performance, and augment safety resilience. Our evaluations, conducted using the Proximal Policy Optimization (PPO) and the HighwayEnv simulation environment, demonstrate noticeable performance improvements with the integration of critical case generation and LLM analysis, indicating CRITICAL's potential to improve the robustness of AV systems and streamline the generation of critical scenarios. This ultimately serves to hasten the development of AV agents, expand the general scope of RL training, and ameliorate validation efforts for AV safety.

cs.RO↗

arXiv2023

Probabilistic Planning for Maritime Search and Rescue

Maritime accidents cause thousands of disappearances every year, with migrant crossings being particularly dangerous and under-reported. Current coastal and NGO search and rescue services are unable to provide a timely response, so new technologies such as autonomous UAVs are needed. We present a thorough formalization of the maritime search and rescue problem considering its time-critical and probabilistic nature. Further, we introduce a method for determining the optimal search altitude for any aerial thermal-based detection system, so as to maximize overall mission success.

eess.SY↗

arXiv2023

Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning

The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55\%), benchmark rewards (25.35\%), best cumulative rewards (24.58\%), optimal actions (13.49\%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.

cs.LG↗

arXiv2022

Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Proximal Policy Optimisation

Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which cannot cover all possible scenarios that can take place in autonomous traffic. Multi-Agent Proximal Policy Optimisation (MAPPO) has recently emerged as a powerful method for autonomous systems because it allows for training in thousands of different situations. In this study, we present an approach based on MAPPO to guarantee the safe and efficient manoeuvring of autonomous vehicles in the presence of an emergency vehicle. We introduce a risk metric that summarises the potential risk of collision in a single index. The proposed method generates cooperative policies allowing the emergency vehicle to go at $15 \%$ higher average speed while maintaining high safety distances. Moreover, we explore the trade-off between safety and traffic efficiency and assess the performance in a competitive scenario.

cs.RO↗

arXiv2022

Feasibility of Equity-driven Taxi Pricing Strategy based on Double Auction Mechanism in Bangkok Metropolitan Region, Thailand

Passenger rejection by taxi drivers impacts the travel behaviour in many cities and suburban areas, often leaving those potential customers in non-popular zones stranded without access to taxis. To overcome this problem, many practices have been implemented, such as penalties to drivers, bans, and new pricing strategies. This paper presents a double auction taxi fare scheme, which gives both passengers and taxi drivers to influence the price, coupled with a clustering method to discourage strategic service rejection in the case study of Bangkok Metropolitan Region, Thailand, which has detailed data availability and uneven taxi journey distributions. The double auction mechanism is tailored to 2019 taxi trips, service rejection complaints, and local travel behaviour to boost transportation equity. To benchmark the performance of the new double auction scheme, a bespoke agent-based model of the taxi service in Bangkok Metropolitan Region at different rejection rates of 0%-20% was created. On one hand, the current rejection behaviour was modelled, and on the other, the double auction pricing strategy was applied. The results indicate that the double auction strategy generates a spatially distributed accessibility and leads to a higher taxi assignment success rate by up to 30%. The double auction scheme increases pickups from locations that are 20-40 km from central Bangkok by 10-15%, despite being areas of low profit. Due to the changing taxi travel landscape and longer taxi journeys, the total air pollutant emissions from the taxis increase by 10% while decreasing local emissions within central areas of Bangkok by upto 40%. Using a 5 Baht average surcharge, the total revenue drops by 20%. The results show that an equity-driven pricing strategy as an implementation of transport policy would be beneficial.

physics.soc-ph↗

arXiv2022

Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real

Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains largely unresolved. Multi-Agent Reinforcement Learning has arisen as a powerful method to accomplish this task because it considers the interaction between agents and also allows for decentralized training -- which makes it highly scalable. However, transferring policies from simulation to the real world is a big challenge, even for single-agent applications. Multi-agent systems add additional complexities to the Sim-to-Real gap due to agent collaboration and environment synchronization. In this paper, we propose a method to transfer multi-agent autonomous driving policies to the real world. For this, we create a multi-agent environment that imitates the dynamics of the Duckietown multi-robot testbed, and train multi-agent policies using the MAPPO algorithm with different levels of domain randomization. We then transfer the trained policies to the Duckietown testbed and compare the use of the MAPPO algorithm against a traditional rule-based method. We show that the rewards of the transferred policies with MAPPO and domain randomization are, on average, 1.85 times superior to the rule-based method. Moreover, we show that different levels of parameter randomization have a substantial impact on the Sim-to-Real gap.

cs.RO↗

arXiv2022

A Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem

Same-Day Delivery services are becoming increasingly popular in recent years. These have been usually modelled by previous studies as a certain class of Dynamic Vehicle Routing Problem (DVRP) where goods must be delivered from a depot to a set of customers in the same day that the orders were placed. Adaptive exact solution methods for DVRPs can become intractable even for small problem instances. In this paper, we formulate the SDDP as a Markov Decision Process (MDP) and solve it using a parameter-sharing Deep Q-Network, which corresponds to a decentralised Multi-Agent Reinforcement Learning (MARL) approach. For this, we create a multi-agent grid-based SDD environment, consisting of multiple vehicles, a central depot and dynamic order generation. In addition, we introduce zone-specific order generation and reward probabilities. We compare the performance of our proposed MARL approach against a Mixed Inter Programming (MIP) solution. Results show that our proposed MARL framework performs on par with MIP-based policy when the number of orders is relatively low. For problem instances with higher order arrival rates, computational results show that the MARL approach underperforms the MIP by up to 30%. The performance gap between both methods becomes smaller when zone-specific parameters are employed. The gap is reduced from 30% to 3% for a 5x5 grid scenario with 30 orders. Execution time results indicate that the MARL approach is, on average, 65 times faster than the MIP-based policy, and therefore may be more advantageous for real-time control, at least for small-sized instances.

cs.MA↗

arXiv2022

Quantitative Risk Indices for Autonomous Vehicle Training Systems

The development of Autonomous Vehicles (AV) presents an opportunity to save and improve lives. However, achieving SAE Level 5 (full) autonomy will require overcoming many technical challenges. There is a gap in the literature regarding the measurement of safety for self-driving systems. Measuring safety and risk is paramount for the generation of useful simulation scenarios for training and validation of autonomous systems. The limitation of current approaches is the dependence on near-crash data. Although near-miss data can substantially increase scarce available accident data, the definition of a near-miss or near-crash is arbitrary. A promising alternative is the introduction of the Responsibility-Sensitive Safety (RSS) model by Shalev-Shwartz et al., which defines safe lateral and longitudinal distances that can guarantee impossibility of collision under reasonable assumptions for vehicle dynamics. We present a framework that extends the RSS model for cases when reasonable assumptions or safe distances are violated. The proposed framework introduces risk indices that quantify the likelihood of a collision by using vehicle dynamics and driver's risk aversion. The present study concludes with proposed experiments for tuning the parameters of the formulated risk indices.

cs.RO↗

arXiv2021

A Reinforcement Learning-based Adaptive Control Model for Future Street Planning, An Algorithm and A Case Study

With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%.

cs.AI↗

arXiv2021

Location-routing Optimisation for Urban Logistics Using Mobile Parcel Locker Based on Hybrid Q-Learning Algorithm

Mobile parcel lockers (MPLs) have been recently introduced by urban logistics operators as a means to reduce traffic congestion and operational cost. Their capability to relocate their position during the day has the potential to improve customer accessibility and convenience (if deployed and planned accordingly), allowing customers to collect parcels at their preferred time among one of the multiple locations. This paper proposes an integer programming model to solve the Location Routing Problem for MPLs to determine the optimal configuration and locker routes. In solving this model, a Hybrid Q-Learning algorithm-based Method (HQM) integrated with global and local search mechanisms is developed, the performance of which is examined for different problem sizes and benchmarked with genetic algorithms. Furthermore, we introduced two route adjustment strategies to resolve stochastic events that may cause delays. The results show that HQM achieves 443.41% improvement on average in solution improvement, compared with the 94.91% improvement of heuristic counterparts, suggesting HQM enables a more efficient search for better solutions. Finally, we identify critical factors that contribute to service delays and investigate their effects.

cs.LG↗

arXiv2020

Vehicle Redistribution in Ride-Sourcing Markets using Convex Minimum Cost Flows

Ride-sourcing platforms often face imbalances in the demand and supply of rides across areas in their operating road-networks. As such, dynamic pricing methods have been used to mediate these demand asymmetries through surge price multipliers, thus incentivising higher driver participation in the market. However, the anticipated commercialisation of autonomous vehicles could transform the current ride-sourcing platforms to fleet operators. The absence of human drivers fosters the need for empty vehicle management to address any vehicle supply deficiencies. Proactive redistribution using integer programming and demand predictive models have been proposed in research to address this problem. A shortcoming of existing models, however, is that they ignore the market structure and underlying customer choice behaviour. As such, current models do not capture the real value of redistribution. To resolve this, we formulate the vehicle redistribution problem as a non-linear minimum cost flow problem which accounts for the relationship of supply and demand of rides, by assuming a customer discrete choice model and a market structure. We demonstrate that this model can have a convex domain, and we introduce an edge splitting algorithm to solve a transformed convex minimum cost flow problem for vehicle redistribution. By testing our model using simulation, we show that our redistribution algorithm can decrease wait times by more than 50%, increase profit up to 10% with less than 20% increase in vehicle mileage. Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.

cs.DS↗

arXiv2020

Identifying Critical Fleet Sizes Using a Novel Agent-Based Modelling Framework for Autonomous Ride-Sourcing

Ride-sourcing platforms enable an on-demand shared transport service by solving decision problems often related to customer matching, pricing and vehicle routing. These problems have been frequently represented using aggregated mathematical models and solved via algorithmic approaches designed by researchers. The increasing complexity of ride-sourcing environments compromises the accuracy of aggregated methods. It, therefore, signals the need for alternative practices such as agent-based models which capture the level of complex dynamics in ride-sourcing systems. The use of these agent-based models to simulate ride-sourcing fleets has been a focal point of many studies; however, this occurred in the absence of a prescribed approach on how to build the models to mimic fleet operations realistically. To bridge this research gap, we provide a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the fundamentals of agent-based modelling theory. We also introduce a model building sequence of the different modules necessary to structure a simulator based on our framework. To showcase the strength of our framework, we use it to tackle the highly non-linear problem of minimum fleet size estimation for autonomous ride-sourcing fleets. We do so by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. By modelling the ride-sourcing fleet function in the urban areas of Manhattan, San Francisco, Paris and Barcelona, we find that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. We also show that pickup wait times have a pivotal role in the estimation of the minimum fleet size in ride-sourcing operations, with agent-based modelling to be a more reliable route for their identification given the system parameters.

eess.SY↗