A systematic review of safety-critical scenarios between automated vehicles and vulnerable road users (original) (raw)
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Vulnerable road users and the coming wave of automated vehicles: Expert perspectives
Transportation Research Interdisciplinary Perspectives, 2021
Automated driving research over the past decades has mostly focused on highway environments. Recent tech- nological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human‐machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed dur- ing the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV‐VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV‐VRU interaction, but they noted that implicit communi- cation will remain dominant and advised against text‐based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakehold- ers and researchers concerned with the relationship between VRUs and AVs in future urban traffic.
Autonomous Driving, 2016
Advancing vehicle automation promises new opportunities to better meet society's future mobility demands. New, extended concepts for interaction with machines are arising in certain areas [1]. A prerequisite for this is further technological development of assistance systems with more capable sensor and information technologies, allowing for a steady automation of driving tasks in vehicle control, right up to self-driving vehicles [2]. Initially the following meta-analysis documents exemplary investigation of potential safety-enhancing vehicle systems with low degrees of automation. However, a safety prognosis of highly or fully automated vehicles depends on assumptions, as so far no series applications of such features exist. For testing methods in order to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining area-wide traffic, accident, weather, and vehicle operation data as well as traffic simulations. Based on these findings, a realistic evaluation of internationally and statistically relevant real world traffic scenarios as well as error processes and stochastic models can be analyzed (in combination with virtual tests in laboratories and driving simulators) to control critical driving situations in the future.
2017
Partially and fully automated vehicles (AVs) are being developed and tested in different countries. These vehicles are being designed to reduce and ultimately eliminate the role of human drivers in the future. Most fatal accidents of vulnerable road users (VRUs), pedestrians, cyclists and mopeds, involve a motorized vehicle. In addition, most of the accidents involving VRUs and motorized vehicles happen at road crossings. By replacing human-driven vehicles with automated vehicles, the human role will be altered and reduced which could lead to an increase in traffic safety. However, drivers are not the only ones who will have to adapt to automated vehicles, other road users, such as pedestrians and cyclists, will have to interact with vehicles with various levels of automation, too. Pedestrians and cyclists will still be humans and might behave in an unpredictable manner which could lead to unsafe behaviors. The main goal of this paper is to propose a theoretical framework which desc...
A review of the interactions between Autonomous Vehicles and Vulnerable Road Users
2019
Recent technological advancements have led to a race in Autonomous Vehicle (AV) developments. One of the issues that is most critical when considering the circulation driverless AVs in roads is their interaction with Vulnerable Road Users (VRUs). The present research aims to investigate the issues of interaction between AVs and VRUs. A literature review from recently published studies around the globe was conducted in order to assess the available technologies and locate possible future trends. The first section examines the topic from the side of AVs and automation technology overall, while the second section approaches issues from the VRU side, such as trust and acceptance. Each section is further divided by examining how different levels of automation will affect the interaction between AVs and VRUs. Results indicate that while low level automation technologies are already proving beneficial, higher level automation seems to be concentrating on compiling AI algorithms that mimic ...
2019
Accidents involving Vulnerable Road Users (VRU) are a very important issue for road safety. The objective of the PROSPECT project is to improve significantly the effectiveness of active VRU safety systems by: (i) expanding the scope of urban scenarios addressed (ii) improving the system performance (iii) proposing extensive validation tools and methodologies for consumer testing, simulation and acceptance studies. Concepts for sensors and control systems were shown in three vehicle demonstrators and a mobile driving simulator and tested with novel VRU dummy specimen. User acceptance tests with the participation of drivers were crucial in PROSPECT for the success of all active safety systems. Driving simulator studies were used in a controlled environment for the collection of data regarding the interaction between the driver and the safety function. A benefit estimation methodology was developed and includes an assessment of the combined effect of active and passive safety measures ...
Road Safety: Human Factors Aspects of Intelligent Vehicle Technologies
Communications in Computer and Information Science, 2018
The design of road-vehicle systems has a crucial impact on the driver's user experience. A post-market trial-and-error approach of the product is not acceptable, as the cost of failure may be fatal. Therefore, to design a suitable system in the automotive context that supports the driver during their journey in an unobtrusive way, a thorough survey of human factors is essential. This article elucidates the broad issues involved in the interaction of road users with intelligent vehicle technologies and summaries of previous work, detailing interaction-design concepts and metrics while focusing on road safety.
SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users
2019 IEEE Intelligent Vehicles Symposium (IV)
This paper presents our research platform SafeVRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasis on the environment perception and planning modules. The environment perception detects the VRUs using a stereo camera and predicts their paths with Dynamic Bayesian Networks (DBNs), which can account for switching dynamics. The motion planner is based on model predictive contouring control (MPCC) and takes into account vehicle dynamics, control objectives (e.g., desired speed), and perceived environment (i.e., the predicted VRU paths with behavioral uncertainties) over a certain time horizon. We present simulation and real-world results to illustrate the ability of our vehicle to plan and execute collision-free trajectories in the presence of VRUs. I. INTRODUCTION Every year between 20 and 50 million people are involved in road accidents, mostly caused by human errors [1]. According to [1], approximately 1.3 million people lost their life in these accidents. Half of the victims are vulnerable road users (VRUs), such as pedestrians and cyclists. Self-driving vehicles can help reduce these fatalities [2]. Active safety features, such as autonomous emergency braking (AEB), are increasingly found on-board vehicles on the market to improve VRUs' safety (see [3] for a recent overview). In addition, some vehicles already automate steering functionality (e.g., [4], [5]), but still require the driver to initiate the maneuver. Major challenges must be addressed to ensure safety and performance while driving in complex urban environments [6], where VRUs are present. The self-driving vehicle should be aware of the presence of the VRUs and be able to infer their intentions to plan its path accordingly to avoid collisions. In this respect, motion planning methods are required to provide safe (collision-free) and systemcompliant performance in complex environments with static and moving obstacles (refer to [7], [8] for an overview). In real-world applications, the information on the pose (i.e., position and orientation) of other traffic participants comes from a perception module. The perception module should provide to the planner information not only concerning the current position of the other road users, but also † The authors equally contributed to the paper.
IEEE Transactions on Intelligent Transportation Systems, 2019
Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.