Three new explorative projects funded through SAFER’s pre-study program
SAFER’s pre-study program continues to deliver interesting ideas for live saving traffic safety research. Rural cycling, safety of emergency vehicles and identification of risky scenarios by using deep learning method will be in focus in the three pre-studies, funded in the first call for 2023.
Rural cycling in focus
The goal of this pre-study is two-fold; first, to provide an overview of the Swedish rural road crash situation for vehicle-to-pedestrian and/cyclist situations, in order to complement the analysis in MICA2 that focused on car-to-cyclist overtaking situations. The second goal is to improve an existing logger that is unobtrusive, easily portable between different bike types, and can be used to collect data from a bicycle point of view. SAFER’s partners VTI and If will be working in this project.
Decreasing drivers’ first interaction delay when using an Emergency Vehicle Approaching system
To improve the safety of emergency vehicles, civilian drivers must move over in a safe and timely manner to give free way. Today, warning lights and sirens are used to warn civilian drivers to move over when an emergency vehicle (EV) is approaching. One proposed method to support drivers when interacting with EVs in traffic is to provide the drivers with a warning. In this study, eye-tracking in combination with measurements of vehicles' speed and placement will allow for a thorough examination of the effects of early warnings for approaching emergency vehicles. VTI and Gothenburg University are collaborating in this project.
Risky scenarios identification by using deep learning method
Most of the accidents are ascribed to risky circumstances such as negligence, unsafe environment, congestion, human error and others. These risky scenarios can be identified and rectified using real-time deep-learning techniques. Identifying the risky scenarios during driving in a real-time manner is crucial for Advanced Driver Assistance System(ADAS) to make active countermeasures in risky scenarios and is also an important component of constructing critical scenarios for evaluating the safety performances of autonomous vehicles (AVs). The planned results include an environmental and driving representation method to quantify the traffic scenarios based on multisource data, including camera data, trajectory data and other high-resolution sensor data. Furthermore, a risk perception assessment framework by using deep learning for evaluating the safety performances of ADAS and AVs in different driving circumstances will be developed.
Chalmers, Volvo and RISE will collaborate with Alkit Communications in this study.
Next call will close in May
Do you have a traffic safety research idea that you would like to develop together with SAFER partners? We have an opportunity through our pre-study program.
Funding will proactively stimulate project generation with several SAFER partners involved to maximize the benefits of our multi-disciplinary platform and obtain a broad commitment, both between partners and within research areas. The funding is for getting started with strategic knowledge creation that can be leveraged e.g., by using the unique, new competence as a basis for national funding or an entry ticket to prestigious international collaborations. We also welcome proposals that are of a different nature, for instance for finalization of publications from a completed project or investigation of a new area.
Simply put, projects that lead SAFER forward and contribute to the continued development of our collaboration platform. The next call will close May 28, 2023. There will also be a call in November 2023.
As a SAFER-partner you can get access to the program here.
Welcome with your ideas!