Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

A new integrated collision risk assessment methodology for autonomous vehicles

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware motion models that take inter-vehicle dependencies into account by utilizing the Bayesian interference are employed to mutually control multiple factors. However, communications between vehicles are often assumed and the developed models are required many parameters to be tuned. Consequently, they are computationally very demanding. Even in the cases where these desiderata are fulfilled, current approaches cannot cope with a large volume of sequential data from organically changing traffic scenarios, especially in highly complex operational environments such as dense urban areas with heterogeneous road users. To overcome these limitations, this paper develops a new risk assessment methodology that integrates a network-level collision estimate with a vehicle-based risk estimate in real-time under the joint framework of interaction-aware motion models and Dynamic Bayesian Networks (DBN). Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. Results indicated an enhancement of the interaction-aware model by up to 10%, when traffic conditions are deemed as collision-prone. Hence, it was concluded that a well-calibrated collision prediction classifier provides a crucial hint for better risk perception by autonomous vehicles

State of the art on measuring driver state and technology-based risk prevention and mitigation Findings from the i-DREAMS project

Advanced vehicle automation and the incorporation of more digital technologies in the task of driving, bring about new challenges in terms of the operator/vehicle/environment framework, where human factors play a crucial role. This paper attempts to consolidate the state-of-the-art in driver state measuring, as well as the corresponding technologies for risk assessment and mitigation, as part of the i-DREAMS project. Initially, the critical indicators for driver profiling with regards to safety risk are identified and the most prominent task complexity indicators are established. This is followed by linking the aforementioned indicators with efficient technologies for real-time measuring and risk assessment and finally a brief overview of interventions modules is outlined in order to prevent and mitigate collision risk. The results of this review will provide an overall multimodal set of factors and technologies for driver monitoring and risk mitigation, essential for road safety researchers and practitioners worldwide<br

Economic evaluation of road user related measures. Deliverable 4.3 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the third deliverable (4.3) of work package 4, which is dedicated to the economic evaluation - mainly by means of a cost-benefit analysis - of road user related safety measures [...continues]

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub

Cyber security and its impact on CAV safety: overview, policy needs and challenges

Connected and Autonomous Vehicles (CAVs) will progressively change the functionality of current transportation systems, promising enhanced safety for all traffic participants. Furthermore, there is a palpable connection between road safety and cyber security breaches, as malicious software could lead to unexpected behavior of CAVs triggering collisions and causing fatalities and injuries. Therefore, policymakers and stakeholders need to take into account knowledge from both CAV safety and cyber security experts, in order to develop the essential regulations. This chapter attempts to link the two domains by reviewing recent approaches with regards to risk assessment for CAVs and cyber security, the impacts of cyber security on road safety and the corresponding challenges. With regards to collision risk assessment, approaches concerning road safety and robotics are mainly reviewed to keep a concise viewpoint on the cybersecurity problem. The main outcomes of this review were that current solutions are solely concerned either with collisions and casualties or with the prevention of cyber-attacks with regards to both hardware and software issues. Consequently, there is still not a reported strong correlation between cyber security breaches and the potential decrease in road safety levels. This should be the focus of policymakers in the near future, so as to develop an holistic policy framework coupling safety and cyber security which will assure a safe and efficient incorporation of CAVs in future transportation systems

Prediction of rear-end conflict frequency using multiple-location traffic parameters

Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating –for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future

Evaluation of safety interventions on risky driving behavior using data from a novel naturalistic driving experiment

This paper aims to evaluate the H2020 project i-DREAMS safety interventions impact on risky driving with a specific focus on speeding events. In this framework, a negative binomial model is developed to examine the correlations between ‘high’ severity speeding events per 100 km where the driver exceeds the proposed speed limit, the safety intervention schemes, and other risky driving factors. Additionally, a Friedman test is conducted to further explore the differences in risky driving behavior among the different intervention schemes. The findings highlight the positive impact of combining real-time and post-trip interventions, in reducing ‘high’ speeding events. Moreover, it is revealed that the presence of harsh acceleration, deceleration, and steering, and fatigue events amplifies the frequency of speeding. Overall, these findings emphasize the efficacy of specific intervention schemes and highlight the importance of addressing multiple risk factors simultaneously to enhance driver behavior and ensure road safety.</p

Evaluation of safety interventions on risky driving behavior using data from a novel naturalistic driving experiment

This paper aims to evaluate the H2020 project i-DREAMS safety interventions impact on risky driving with a specific focus on speeding events. In this framework, a negative binomial model is developed to examine the correlations between ‘high’ severity speeding events per 100 km where the driver exceeds the proposed speed limit, the safety intervention schemes, and other risky driving factors. Additionally, a Friedman test is conducted to further explore the differences in risky driving behavior among the different intervention schemes. The findings highlight the positive impact of combining real-time and post-trip interventions, in reducing ‘high’ speeding events. Moreover, it is revealed that the presence of harsh acceleration, deceleration, and steering, and fatigue events amplifies the frequency of speeding. Overall, these findings emphasize the efficacy of specific intervention schemes and highlight the importance of addressing multiple risk factors simultaneously to enhance driver behavior and ensure road safety.</p

D3.1 Framework for operational design of experimental work in i-DREAMS

i-DREAMS is divided into five broad technical work areas: State of the art (monitoring and interventions), Methodological development, Technology development, Trials, and Analysis.This report is the first of the methodological work area of i-DREAMS. The methodological work area is concerned with the definition of a context-aware Safety Tolerance Zone and the development of a driver and environment assessment and monitoring system that will assist drivers in staying within this Safety Tolerance Zone (see Figure 1). The work will be informed by the state of the art (monitoring and interventions) work area and in particular:The state of the art for impact in terms of safety, and the measurement tools for variables that could be used within the monitoring platform.The state of the art for methodologies and tools to provide real time interventions and post trip interventions with the aim of increasing safe driver behaviour.It will also provide information to the technology development and trials work areas which will test and validate the system. Specifically, it will inform the assessment of the available technology for variable collection and how these can be integrated into the i-DREAMS platform. </div