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Technical Paper

An ADAS-Oriented Virtual EPS Platform Based on the Force Feedback Actuator of the Steer-by-Wire System

Electric Power Steering (EPS) is the actuator of several lateral-dynamic-related Advanced Driver Assistance Systems (ADAS). A driving simulator with EPS will be much helpful for the ADAS development. However, if a real EPS is used in the driving simulator, it is quite difficult to realize the road reaction force accurately and responsively. To overcome this weakness, a virtual EPS platform is established. The virtual EPS platform contains two parts: one is the vehicle and EPS model, the other is the force feedback actuator (FFA) of the Steer-by-Wire (SBW) system. The FFA is an interface between the driver and the EPS/vehicle model. The reactive torque of the FFA is obtained based on the models. Meanwhile, the input of the EPS model is the steering angle of the FFA. Comparing to a real EPS, the virtual EPS platform has a problem of instability because of the actuator lag of the FFA. Therefore, a damping control method is applied to make the system stable.
Technical Paper

Driver Lane Keeping Characteristic Indices for Personalized Lane Keeping Assistance System

In the recent years, the interaction between human driver and Advanced Driver Assistance System (ADAS) has gradually aroused people’s concern. As a result, the concept of personalized ADAS is being put forward. As an important system of ADAS, Lane Keeping Assistance System (LKAS) also attracts great attention. To achieve personalized LKAS, driver lane keeping characteristic (DLKC) indices which could distinguish different driver lane keeping behavior should be researched. However, there are few researches on DLKC indices for personalized LKAS. Although there are many researches on modeling driver steering behavior, these researches are not sufficient to obtain DLKC indices. One reason is that most of researches are for double lane change behavior which is different from driver lane keeping behavior. The other reason is that the researches on driver lane keeping behavior only provide model structure and rarely discuss identification procedure such as how to select suitable data.
Technical Paper

Multi-target Tracking Algorithm with Adaptive Motion Model for Autonomous Urban Driving

Since situational awareness is crucial for autonomous driving in urban environments, multi-target tracking has become an increasingly popular research topic during the last several years. For autonomous driving in urban environments, cars and pedestrians are the two main types of obstacles, and their motion characteristics are not the same. While in the current related multi-target tracking research, the same motion model (such as Constant Velocity model [CV]) or motion model set (such as CV combined with Constant Acceleration model [CA]) is mostly used to track different types of obstacles simultaneously. Besides, in current research, regular motion models are mostly adopted to track pedestrians, such as CV, CA, and so on, the uncertainty in pedestrian motion is not well considered.