ManeuverStack - A Stacked Vehicle Maneuver Prediction Model

Vehicle manufacturers are introducing advanced safety and driving aids by incorporating sensors like LiDAR, RADAR, cameras, etc. Further, there is an increased regulatory pressure to incorporate features like automatic emergency braking and collision warning in all vehicles. These require vehicles to have data-driven prediction systems that can accurately predict maneuvers and unexpected behaviors of surrounding vehicles. We propose ManeuverStack, a Stacked Bidirectional Long Short Term Memory (LSTM) model for maneuver prediction. This online model continuously collects surrounding vehicles’ data and predicts their maneuver in the next 0.5 to 5 seconds. The model has been trained and tested on a mix of six datasets which had different road characteristics and vehicle classes. Its maximum accuracy was 95%. It has been benchmarked against six state-of-the-art models available in the literature. The model’s accuracy, recall and F1 scores outperformed most models while being at par with models which predict fewer maneuver classes. The model also performed on par with the best benchmark in crash avoidance scenarios. The model’s high accuracy ensured 100% crash avoidance even at deceleration lower than the standard recommended value for passenger comfort. When predicting maneuvers for high-risk scenarios, the model provided higher accuracy than most of the benchmark models. Further deployment and testing of the model on an edge device revealed that the model’s response time is at least two orders of magnitude faster than the minimum required prediction window.