PUVAME - New French Approach for Vulnerable Road Users Safety (original) (raw)
Related papers
2011
Current research in Intelligent Transport Systems is focussing on informing cars to make traffic more efficient and safer. However vulnerable road users are not involved or informed. In order to prevent collisions with vulnerable road users cars make use of collision avoidance systems. These systems are mainly based on camera, infrared and/or radar sensors and suffer from difficulties like road users hidden by cars and buildings. These collision prevention systems are mainly autonomous systems and are not cooperating with other detection systems. We propose to extend CALM, the communication standard for cooperative vehicular systems (16), with a local communication system for protection devices for vulnerable road users. These systems can be integrated into existing systems like bicycle navigation systems and/or smartphones. For devices which cannot rely on a navigation system we propose to use position estimation, based on the position of neighbouring devices, like parked cars, roadside units or smart devices.
SAVE-U: An innovative sensor platform for vulnerable road user protection
2003
Among other initiatives to improve safety of Vulnerable Road Users (VRUs), the European Commission is funding a research project called SAVE-U (IST-2001-34040): "Sensors and system architecture for VulnerablE road Users protection" aimed at developing an integrated safety concept for pedestrians and cyclists. SAVE-U started in March 2002 and will last 3 years. This paper provides an overview of the results of work performed along the first year of the project.
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.
Platform enabling intelligent safety applications for vulnerable road users
IET Intelligent Transport Systems, 2014
In 2009, 9108 vulnerable road users (VRUs; pedestrians and bicyclists) died in the EU and 4722 in the US. Active safety systems, that is intelligent systems able to predict and prevent crashes, may significantly help to reduce VRU fatalities and injuries; however, current active safety systems for VRUs are only found on high-end vehicles, only support the host vehicle driver, and do not make use of wireless communication. The scope of this study is to describe the setup and realworld verification of a platform to enable active safety systems for VRU. This platform is carried by VRUs and may support multiple road users using wireless communication. A simple conceptual application, addressing pedestrian safety at crossings, was developed to test the platform. This application was not cooperative (i.e. did not support multiple road users with wireless communication). The results presented in this study suggest that such a platform can be employed (i) as a logger for naturalistic studies on VRUs, (ii) to better understand VRU behaviour and accident causation and (iii) as a basis for the development of novel active safety applications, running on portable devices, such as future generation smart phones, and possibly enabled by wireless communication.
Numerical Technologies for Vulnerable Road User Safety Enhancement.pdf
The progress in pedestrian and cyclist safety enhancement is the result of multi-stage work, which bases mainly on the appropriate traffic organization and road engineering. However, the full separation of vehicle traffic and pedestrians/cyclists seems to be unmanageable nowadays. Thus, the paper presents a dual approach for vulnerable road user safety enhancement by the use of state-of-the-art numerical technologies. Firstly, the detection technologies are presented which observe the vehicles environment in order to detect, track and classify the surrounding objects, providing data for active safety systems and as well as vehicle's driver. Their system architectures also create communication interface between a human and automobile via the accident-avoidance technology and pre-crash sensing. Secondly, when the collision is unavoidable, the passive safety structures and systems are in operation aimed at pedestrian/cyclist injuries mitigation. Hence, the authors carried out passive safety virtual simulations to evaluate the response of the human body after a vehicle impact.
Numerical Technologies for Vulnerable Road User Safety Enhancement
The progress in pedestrian and cyclist safety enhancement is the result of multi-stage work, which bases mainly on the appropriate traffic organization and road engineering. However, the full separation of vehicle traffic and pedestrians/cyclists seems to be unmanageable nowadays. Thus, the paper presents a dual approach for vulnerable road user safety enhancement by the use of state-of-the-art numerical technologies. Firstly, the detection technologies are presented which observe the vehicles environment in order to detect, track and classify the surrounding objects, providing data for active safety systems and as well as vehicle's driver. Their system architectures also create communication interface between a human and automobile via the accident-avoidance technology and pre-crash sensing. Secondly, when the collision is unavoidable, the passive safety structures and systems are in operation aimed at pedestrian/cyclist injuries mitigation. Hence, the authors carried out passive safety virtual simulations to evaluate the response of the human body after a vehicle impact.
Study on Perception and Communication Systems for Safety of Vulnerable Road Users
2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015
The existing R&D efforts for protecting vulnerable road users (VRU) are mainly based on perception techniques, which aim to detect VRUs utilizing vehicle embedded sensors. The efficiency of such a technique is largely affected by the sensor's visibility condition. Vehicle-to-Pedestrian (V2P) communication can also contribute to the VRU safety by allowing vehicles and pedestrians to exchange information. This solution is, however, largely affected by the reliability of the exchanged information, which most generally is the GPS data. Since perception and communication have complementary features, we can expect that a combination of such approaches can be a solution to the VRU safety. This is the motivation of the current work. We develop theoretical models to present the characteristics of perception and communications systems. Experimental studies are conducted to compare the performances of these techniques in real-world environments. Our results show that the perception system reliably detects pedestrians and other objects within 50 m of range in the line-of-sight (LOS) condition. In contrast, the V2P communication coverage is approximately 340 and 200 meters in LOS and non-LOS (NLOS) conditions, respectively. However, the communication-based system fails to correctly position the VRU w.r.t the vehicle, preventing the system from meeting the safety requirement. Finally, we propose a cooperative system that combines the outputs of the communication and perception systems.
Vulnerable Road User Protection from Heavy Goods Vehicles Using Direct and Indirect Vision Aids
Sustainability, 2022
In Europe, heavy goods vehicles (HGVs) are disproportionately involved in serious and fatal collisions with vulnerable road users (VRUs). An interrogation of 2019 national crash data for Great Britain (Stats19) suggested that detection of cyclists and pedestrians in the nearside and front blind spots of HGVs is still a significant problem during forward or left-turn manoeuvres of the HGV. To improve detection, Transport for London introduced Direct Vision and Safe System Standards in 2021 for HGVs entering the Greater London area. This research assessed the efficacy of one of the Safe System requirements—the fitment of sensors to detect vulnerable road users on the nearside of the vehicle. A physical testing procedure was developed to determine the performance of a sensor system meeting the Transport for London Safe System requirements. Overall, the Safe System compliant sensor system missed 52% of expected detection nodes on the nearside of the vehicle. A total of 56% of the “stop ...