Lane Departure Warning Mechanism of Limited False Alarm Rate using Extreme Learning Residual Network and xcfxb5greedy LSTM Neglecting the driver behavioral model in lanedeparturewarning systems has taken over as the primary reason for false warnings in humanxe2x80x93machine interfaces. We propose a machine learningbased mechanism to identify driversxe2x80x99 unintended lanedeparture behaviors, and simultaneously predict the possibility of driver proactive correction after slight departure. First, a deep residual network for driving state feature extraction is established by combining time series sensor data and three serial ReLU residual modules. Based on this feature network, online extreme learning machine is organized to identify a driverxe2x80x99s behavior intention, such as unconscious lanedeparture and intentional lanechanging. Once the system senses unconscious lanedeparture before crossing the outermost warning boundary, the xcfxb5greedy LSTM module in shadow mode is roused to verify the chances of driving the vehicle back to the original lane. Only those unconscious lanedepartures with no driversxe2x80x99 proactive correction behavior are transferred into the warning module, guaranteeing that the system has a limited false alarm rate. In addition, naturalistic driving data of twentyone drivers are collected to validate the system performance. Compared with the basic timetolinecrossing (TLC) method and the TLCDSPLS method, the proposed warning mechanism shows a largescale reduction of 12.9% on false alarm rate while maintaining the competitive accuracy rate of about 98.8%.

Conditional Artificial Potential FieldBased Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario Carfollowing is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the carfollowing control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehiclesxe2x80x99 abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learningbased lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an intervalbased support vector machine is designed to predict the vehiclesxe2x80x99 lane change intention utilizing limited lowlevel vehicle status through vehicletovehicle communication. Then, a conditional artificial potential field method is used to design the carfollowing controller by incorporating the lanechange intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehiclexe2x80x99s lane change intention more accurately. The autonomous vehicle avoids collisions with a lanechanging connected unintelligent vehicle with reliable safety and favorable dynamic performance.