Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

Bronstein, Eli; Palatucci, Mark; Notz, Dominik; White, Brandyn; Kuefler, Alex; Lu, Yiren; Paul, Supratik; Nikdel, Payam; Mougin, Paul; Chen, Hongge; Fu, Justin; Abrams, Austin; Shah, Punit; Racah, Evan; Frenkel, Benjamin; Whiteson, Shimon; Anguelov, Dragomir

doi:10.1109/iros47612.2022.9981695

Cited by 25 publications

(11 citation statements)

References 25 publications

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“…There are two main approaches to end-to-end self-driving for planning and control tasks (how-to-act): 1) Imitation Learning: in which an agent learns to mimic the behaviour of an expert. [146][147][148] 2) Deep Reinforcement Learning (DRL): in which an agent tries to learn how to act in a trial-and-error process that typically takes place in a simulated environment. DRL methods will be analysed in greater detail in Section 6.…”

Section: Learning Based Methodsmentioning

confidence: 99%

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

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Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

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Section: Learning Based Methodsmentioning

confidence: 99%

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In [25], a hierarchical model-based GAIL was proposed to solve the autonomous driving problem in a differentiable simulator. The project used a graph-based search algorithm to generate the autonomous vehicle trajectory, which was combined with roadgraph points, traffic light signals, and other objects' trajectories to form the input for the policy and discriminator transformer-based networks.…”

Section: Gailmentioning

confidence: 99%

Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments

Couto

Antonelo

2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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Deriving robust control policies for realistic urban navigation scenarios is not a trivial task. In an end-to-end approach, these policies must map high-dimensional images from the vehicle's cameras to low-level actions such as steering and throttle. While pure Reinforcement Learning (RL) approaches are based exclusively on rewards, Generative Adversarial Imitation Learning (GAIL) agents learn from expert demonstrations while interacting with the environment, which favors GAIL on tasks for which a reward signal is difficult to derive. In this work, the hGAIL architecture was proposed to solve the autonomous navigation of a vehicle in an end-to-end approach, mapping sensory perceptions directly to low-level actions, while simultaneously learning mid-level input representations of the agent's environment. The proposed hGAIL consists of an hierarchical Adversarial Imitation Learning architecture composed of two main modules: the GAN (Generative Adversarial Nets) which generates the Bird's-Eye View (BEV) representation mainly from the images of three frontal cameras of the vehicle, and the GAIL which learns to control the vehicle based mainly on the BEV predictions from the GAN as input. Our experiments have shown that GAIL exclusively from cameras (without BEV) fails to even learn the task, while hGAIL, after training, was able to autonomously navigate successfully in all intersections of the city.

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“…3D object detection in LiDAR point clouds is an essential task in an autonomous driving system, as it provides crucial information for subsequent onboard modules, ranging from perception [24,25], prediction [27,41] to planning [3,23]. There have been extensive research efforts on developing sophisticated networks that are specifically designed to cope with point clouds in this field [14,31,32,42,48].…”

Section: Introductionmentioning

confidence: 99%

FuseNet: 3D Object Detection Network with Fused Information for Lidar Point Clouds

Liu

Tian

Wang

et al. 2022

Neural Process Lett

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In order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object detection research mostly focuses on designing dedicated local point aggregators for fine-grained geometrical modeling. In this paper, we revisit the local point aggregators from the perspective of allocating computational resources. We find that the simplest pillar based models perform surprisingly well considering both accuracy and latency. Additionally, we show that minimal adaptions from the success of 2D object detection, such as enlarging receptive field, significantly boost the performance. Extensive experiments reveal that our pillar based networks with modernized designs in terms of architecture and training render the state-of-the-art performance on the two popular benchmarks: Waymo Open Dataset and nuScenes. Our results challenge the common intuition that the detailed geometry modeling is essential to achieve high performance for 3D object detection.

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Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

Cited by 25 publications

References 25 publications

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments

FuseNet: 3D Object Detection Network with Fused Information for Lidar Point Clouds

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