An Efficient Deep Spatio-Temporal Context Aware Decision Network (DST-CAN) for Predictive Manoeuvre Planning on Highways

The safety and efficiency of an Autonomous Vehicle (AV) manoeuvre planning heavily depend on the future trajectories of surrounding vehicles. If an AV can predict its surrounding vehicles’ future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) for predictive manoeuvre decisions for AVs on highways. DST-CAN has two main components, namely spatio-temporal context-aware map generator and predictive manoeuvre decisions engine. DST-CAN employ a memory neuron network to predict the future trajectories of its surrounding vehicles. Using look-ahead prediction and past actual trajectories, a spatio-temporal context-aware probability occupancy map is generated. These context-aware maps as input to a decision engine generate a safe and efficient manoeuvre decision. Here, CNN helps extract feature space, and two fully connected network generates longitudinal and lateral manoeuvre decisions. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 highway datasets. A traffic rule is defined to generate ground truths for these datasets in addition to human decisions. Two DST-CAN models are trained using imitation learning with human driving decisions from actual traffic data and rule-based ground truth decisions. The performances of the DST-CAN models are compared with the state-of-the-art Convolutional Social-LSTM (CS-LSTM) models for manoeuvre prediction. The results clearly indicate that the context-aware maps help DST-CAN to predict the decision accurately over CS-LSTM. Further, an ablation study has been carried out to understand the effect of prediction horizons of performance and a robustness study to understand the near collision scenarios over actual traffic observations. The context-aware map with a 3 second prediction horizon is robust against near collision.

I. Introduction

It is very crucial for an Autonomous Vehicle (AV) to understand the intentions of its surrounding vehicles based on their past and present contextual information for safe and efficient decision-making. These intentions can be inferred by predicting the future trajectories of surrounding vehicles. However, the dynamic nature of the context of the AV and the uncertainties in the prediction [1] of surrounding vehicles’ trajectories pose a significant challenge in manoeuvre planning that may lead to near-collision scenarios. A Predictive Manoeuvre Planning (PMP) approach that can incorporate the contextual information with uncertainties in predicting neighbouring vehicles’ trajectories is crucial for safe and efficient manoeuvre planning.