With the increase of robots navigating through crowded environments in our daily lives, the demand for designing a socially-aware navigation method considering humanrobot interaction has risen. When developing and assessing socially-aware navigation methods, pedestrian motion modeling plays a significant role. However, existing pedestrian models often struggle in complex environments and do not have the capacity to generate diverse pedestrian styles.In this paper, we propose multi-agent reinforcement learning with local coordination for individual diversity (MAC-ID), which can synthesize diverse pedestrian motions via local coordination factor (LCF). Our experiments have demonstrated that the manipulation of the LCF induces interpretable changes in pedestrian behaviors, along with a superior performance compared to existing pedestrian motion models. For evaluating socially-aware navigation methods using MAC-ID, we present a novel benchmark called BSON. It offers realistic and diverse social environments with pedestrians modeled via MAC-ID. We have trained and compared various navigation methods in BSON using a newly proposed metric called socially-aware navigation score (SNS). Through BSON, users can evaluate their socially-aware navigation methods and compare them to baselines.

Designing a socially-aware navigation method for crowded environments has become a critical issue in robotics. In order to perform navigation in a crowded environment without causing discomfort to nearby pedestrians, it is necessary to design a global planner that is able to consider both human-robot interaction (HRI) and prediction of future states. In this paper, we propose a socially-aware global planner called SCAN, which is a global planner that generates appropriate local goals considering HRI and prediction of future states. Our method simulates future states considering the effects of the robot's actions on the future intentions of pedestrians using Monte Carlo tree search (MCTS), which estimates the quality of local goals. For fast simulation, we execute pedestrian motion prediction using Y-net and future state simulation using MCTS in parallel. Neural networks are only used in Y-net and not in MCTS, which enables fast simulation and prediction of a long horizon of future states. We evaluate the proposed method based on the proposed socially-aware navigation metric using realistic pedestrian simulation and real-world experiments. The results show that the proposed method outperforms existing methods significantly, indicating the importance of considering human-robot interaction for socially-aware navigation.

In this paper, we present a novel autonomous driving framework, called a road graph and image attention network (RIANet), which computes the attention scores of objects in the image using the road graph feature. The process of the proposed method is as follows: First, the feature encoder module encodes the road graph, image, and additional features of the scene. The attention network module then incorporates the encoded features and computes the scene context feature via the attention mechanism. Finally, the low-level controller mod-ule drives the ego-vehicle based on the scene context feature. In the experiments, we use an urban scene driving simulator named CARLA to train and test the proposed method. The results show that the proposed method outperforms existing autonomous driving methods.

Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection problem, becomes crucial. However, conventional datasets often only contain expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. To this end, we present a dataset called the R3 Driving Dataset, composed of driving data with different qualities. The dataset categorizes abnormal driving behaviors into eight categories and 369 different detailed situations. The situations include dangerous lane changes and near-collision situations. To further enlighten how these abnormal driving behaviors can be detected, we utilize different uncertainty estimation and anomaly detection methods for the proposed dataset. From the results of the proposed experiment, it can be inferred that by using both uncertainty estimation and anomaly detection, most of the abnormal cases in the proposed dataset can be discriminated. https://rllab-snu.github.io/projects/R3-Driving-Dataset/doc.html