Abstract Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance.
Abstract Autonomous driving has gained significant advancements in recent years. However, obtaining a robust control policy for driving remains challenging as it requires training data from a variety of scenarios, including rare situations (e.g., accidents), an effective policy architecture, and an efficient learning mechanism. We propose ADAPS for producing robust control policies for autonomous vehicles. ADAPS consists of two simulation platforms in generating and analyzing accidents to automatically produce labeled training data, and a memoryenabled hierarchical control policy.
Overview We present novel algorithms for identifying emotion, dominance, and friendliness characteristics of pedestrians based on their motion behaviors. We also propose models for conveying emotions, friendliness, and dominance traits in virtual agents. We present applications of our algorithms to simulate interpersonal relationships between virtual characters, facilitate socially-aware robot navigation, identify perceived emotions from videos of walking individuals, and increase the sense of presence in scenarios involving multiple virtual agents.
Abstract We present a real-time algorithm to automatically classify the dynamic behavior or personality of a pedestrian based on his or her movements in a crowd video. We present a statistical scheme that dynamically learns the behavior of every pedestrian in a scene and computes that pedestrian’s motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics, which can also be used to predict the crowd movement and behavior.
Abstract We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes. We present a novel CTMAT representation based on medial axis transform to compute a tight fitting bounding shape for each agent, which is less conservative and results in fewer false collisions. The overall runtime performance is comparable to prior multi-agent collision avoidance algorithms that use circular or elliptical agents. Based on CTMAT representation, we present a novel algorithm AutoRVO for computing collision-free navigation for heterogeneous road-agents such as cars, tricycles, bicycles, and pedestrians in dense traffic.
– Overview Autonomous Driving has become one of the most anticipated technologies in both industry and academic research groups. Most of the current efforts in autonomous driving have found success in idealistic conditions such as sparse and homogeneous traffic on highways and urban areas. The GAMMA group, instead, aims to advance autonomous driving research in highly dense and heterogeneous traffic conditions that characterizes social and psychological aspects of human drivers in uncertain environments.
Abstract We present AutonoVi, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance.
Abstract We present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles. We propose a novel set of features that can be easily extracted from car trajectories. We derive a data-driven mapping between these features and six driver behaviors using an elaborate web-based user study. We also compute a summarized score indicating a level of awareness that is needed while driving next to other vehicles.