arXiv subjects

Mohammed Quddus

Publications and source records attributed to Mohammed Quddus.

5 recordsLinked to original sources

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

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↗

arXiv2022

Developing a Vehicle Re-routing Algorithm using Connected Vehicle (CV) Technology

Vehicle Ad-hoc Networks (VANETs) act as the core of vehicular communications and provide the fundamental wireless communication architecture to support both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Therefore, by leveraging only communication technologies, Connected Vehicles (CVs) can navigate through the dynamic road network. However, such vehicles are still in their infancy but are expected to have a significant impact on safety and mobility such as reducing non-recurrent congestion in case of a vehicle breakdown or other roadway incidents. To evaluate their impacts, this research examines the benefits of having CVs when a vehicle breakdown occurs by developing an intelligent proactive re-routing algorithm. Due to a lack of real-world data, this paper adopts an integrated simulated framework consisting of a V2X (OMNET++) communication simulator and a traffic microscopic simulator (SUMO). The developed algorithm functions such that when a vehicle is broken down within a live traffic lane, the system detects the breakdown, generates warning messages immediately and transmits them to approaching vehicles. Based on the real-time notification, informed vehicles proactively re-route to alternative roads to avoid the breakdown zone. Two scenarios were developed where a breakdown occurs within and outside a junction for both V2X-enabled and disabled systems. Results show that V2X-enabled CV re-routing mechanism can improve traffic efficiency by reducing congestion and enhance traffic safety by smoothing accelerations and decelerations of affected vehicles with low infrastructure costs. The algorithm would be useful to highway agencies (Department for Transport) and vehicle manufacturers in introducing CVs onto existing road networks.

cs.NI↗

arXiv2020

TDMP-Reliable Target Driven and Mobility Prediction based Routing Protocol in Complex VANET

Vehicle-to-everything (V2X) communication in the vehicular ad hoc network (VANET), an infrastructure-free mechanism, has emerged as a crucial component in the advanced Intelligent Transport System (ITS) for special information transmission and inter-vehicular communications. One of the main research challenges in VANET is the design and implementation of network routing protocols which manage to trigger V2X communication with the reliable end-to-end connectivity and efficient packet transmission. The organically changing nature of road transport vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packet delivery. Therefore, a position-based routing protocol tends to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. In this paper, a target-driven and mobility prediction (TDMP) based routing protocol is therefore developed for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. The primary idea in TDMP is that the destination target of a driver is included in the mobility prediction to assist the implementation of the routing protocol. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, TDMP is developed to enhance the packet transmission with the consideration of the estimation of inter-vehicles link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout.

cs.NI↗