A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system.

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As one of important advanced driving assistance systems (ADAS) for intelligent vehicles, forward collision warning system is an effective solution to avoid the traffic accidents. Accurately trajectory prediction can obtain the future spatial distribution of the vehicles in contextual traffic, which will help the driver make the decisions and avoid potential collisions. This paper proposes an effective trajectory prediction method based on long short term memory model integrated with attention mechanism and regularization strategy (AR-LSTM). The long short-term memory and convolutional neural network (LSTM-CNN) is utilized to recognize the driver’s lane change intention. Combined with the multi-modal information of the vehicles such as vehicle status variables, traffic information, and driver’s lane change intention, the AR-LSTM model is designed to predict the vehicle’s future trajectory. Besides, the AR-LSTM and LSTM- CNN model are trained and tested by the real traffic data set NGSIM (next generation simulation). Finally, considering two maneuvers to avoid the collisions by braking or changing lane, the proposed forward collision warning system is verified by the hardware-in-loop (HIL) platform compared to the existed fixed TTC (time to collision) method. And the statistical results demonstrate that the proposed system can provide correct warnings of braking and decrease invalid warnings of braking.

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