In recent years, intelligent vehicles have been at the center of research in automotive engineering and the future direction of the automotive industry, and are gradually showing a trend towards practicality. Excellent environmental perception and detection capability is the prerequisite and foundation for the normal operation of driverless vehicles, which directly affects the realisation of subsequent planning and decision-making, control execution and other advanced functions. This paper introduces the development status of intelligent vehicles at home and abroad, studies the current research status of mainstream perception algorithms, analyses the technical challenges encountered in perception for intelligent vehicles in urban roads, and illustrates the development goal of establishing efficient and reliable environmental perception algorithms in complex scenarios.
Pedestrians are the most important vulnerable road traffic participant. According to the traffic accident survey, pedestrian fatalities exceed 30% of total fatalities. Among the accident scenarios of car to pedestrian, car to pedestrian crossing and car to pedestrian longitudinal are the most frequent. Research shows that the accident rate is higher at nighttime than daytime. In this paper, based on the characteristics of road traffic accidents in China and combined with the nighttime illumination characteristics of Chinese roads, we focused on the study of automatic emergency braking (AEB) system for pedestrian nighttime test and evaluation scenarios, and carried out the validation of test methods and test techniques.
As the main component function of ADAS, the assessment methods on AEB have been guiding OEMs to make functional improvements and the active safety capability of the vehicle has been increasing. By screening the car-to-car accident data in China, key factors such as offset situation, vehicle speed distribution and accident severity have been analyzed and counted. Subsequently, the corresponding weights have been reasonably determined. Finally, a scientific AEB car-to-car assessment method has been obtained, forming the relevant part of the C-NCAP 2021 Edition. The test method proposed in this paper can objectively and reasonably evaluate the AEB performance of vehicles.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.