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.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

A simulation study of predicting conflict-prone traffic conditions in real-time

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing the traffic conditions just prior to collisions with the traffic conditions during normal operations. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the aggregated traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. This may subsequently lead to an incorrect calibration of the model used to predict the probability of a collision. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data (i.e. vehicle trajectories) from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts (i.e. dangerous concurrences between vehicles) data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety, and data on traffic collisions are therefore not needed. Two classifiers are then employed to examine the proposed idea: (i) Support Vector Machines (SVMs) – a sophisticated classifier and (ii) k-Nearest Neighbors (kNN) – a relatively simple classifier. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of 3-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective conflicts prediction

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

Real-time classification of aggregated traffic conditions using relevance vector machines

This paper examines the theory and application of a recently developed machine learning technique namely Relevance Vector Machines (RVMs) in the task of traffic conditions classification. Traffic conditions are labelled as dangerous (i.e. probably leading to a collision) and safe (i.e. a normal driving) based on 15-minute measurements of average speed and volume. Two different RVM algorithms are trained with two real-world datasets and validated with one real-world dataset describing traffic conditions of a motorway and two A-class roads in the UK. The performance of these classifiers is compared to the popular and successfully applied technique of Support vector machines (SVMs). The main findings indicate that RVMs could successfully be employed in real-time classification of traffic conditions. They rely on a fewer number of decision vectors, their training time could be reduced to the level of seconds and their classification rates are similar to those of SVMs. However, RVM algorithms with a larger training dataset consisting of highly disaggregated traffic data, as well as the incorporation of other traffic or network variables so as to better describe traffic dynamics, may lead to higher classification accuracy than the one presented in this paper

A simulation study of predicting real-time conflict-prone traffic conditions

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative to pre-collision traffic dynamics. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e. Support Vector Machines, k-Nearest Neighbours and Random Forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of Random Forests with 5-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective prediction of conflicts

Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

SHOW Deliverable 10.1: Simulation scenarios and tools

This document identifies all simulation tools which are used by all partners participating in Work Package 10 of the SHOW project. Their applications range from vehicle level of shared CCAVs up to mobility level, and they are used to enrich all field experiment results of the SHOW pilots. In addition, a relation of tools to application areas and to SHOW pilots is presented. Furthermore, multiple simulation scenarios are introduced, which define the used tools to evaluate the scenario, their expected results as well as the addressed KPIs from A9.4. After a short presentation of the SHOW sites that are investigated in simulation in this WP, the simulation plans of all participating partners are presented and linked to at least one of the pilot sites. Additionally, data inputs that are required from the SHOW sites are stated

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]