A Cognitive Urban Collision Avoidance Framework Based on Agents Priority Using Recurrent Neural Network We propose a novel cognitive collision avoidance (CA) framework for autonomous driving (AD) vehicles in urban environments. In this framework, a hybrid future trajectory predictor is developed, which consists of a static agent classifier, a recurrent neural network (RNN) based trajectory predictor and a lanebased kinematic model predictor. To fuse the outputs of different predictors, an iterative multivariate Gaussian weighted algorithm is designed to drop outliers and estimate the predicted dynamic features more reliably. Subsequently, fed in with the fused results of observed agents, together with the current dynamic features and planned trajectory of the ego vehicle, an RNNbased priority prediction engine is applied to infer the priority probabilities distribution for CA decision, which indicates the likelihood that the vehicle continue driving according to its planned trajectory. By observing surrounding agentsu0027 historical ground truth trajectory and taking the road geometry constraints into consideration, the future dynamic features, priority probabilities distribution and the CA decision can be figured out at every timestamp cognitively and adaptively. The performance of this framework is evaluated on a prototype car in multiple typical USA urban scenarios, comparing with conventional CA systems which assume constant velocity and only work when observed agents follow traffic rules, our framework alleviates these limitations and achieves encouraging results in terms of the priority distribution estimation, with a frequency u003e20Hz, which is capable of running in realtime.

Agent Prioritization for Autonomous Navigation In autonomous navigation, a planning system reasons about other agents to plan a safe and plausible trajectory. Before planning starts, agents are typically processed with computationally intensive models for recognition, tracking, motion estimation and prediction. With limited computational resources and a large number of agents to process in real time, it becomes important to efficiently rank agents according to their impact on the decision making process. This allows spending more time processing the most important agents. We propose a system to rank agents around an autonomous vehicle (AV) in real time. We automatically generate a ranking data set by running the planner in simulation on realworld logged data, where we can afford to run more accurate and expensive models on all the agents. The causes of various planner actions are logged and used for assigning ground truth importance scores. The generated data set can be used to learn ranking models. In particular, we show the utility of combining learned features, via a convolutional neural network, with engineered features designed to capture domain knowledge. We show the benefits of various design choices experimentally. When tested on real AVs, our system demonstrates the capability of understanding complex driving situations.