1,964 research outputs found
Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform
Autonomous Driving vehicles (ADV) are on road with large scales. For safe and
efficient operations, ADVs must be able to predict the future states and
iterative with road entities in complex, real-world driving scenarios. How to
migrate a well-trained prediction model from one geo-fenced area to another is
essential in scaling the ADV operation and is difficult most of the time since
the terrains, traffic rules, entities distributions, driving/walking patterns
would be largely different in different geo-fenced operation areas. In this
paper, we introduce a highly automated learning-based prediction model
pipeline, which has been deployed on Baidu Apollo self-driving platform, to
support different prediction learning sub-modules' data annotation, feature
extraction, model training/tuning and deployment. This pipeline is completely
automatic without any human intervention and shows an up to 400\% efficiency
increase in parameter tuning, when deployed at scale in different scenarios
across nations.Comment: Accepted by the 31st IEEE Intelligent Vehicles Symposium (2020
SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections
In this work we propose a deep learning model, i.e., SAPI, to predict vehicle
trajectories at intersections. SAPI uses an abstract way to represent and
encode surrounding environment by utilizing information from real-time map,
right-of-way, and surrounding traffic. The proposed model consists of two
convolutional network (CNN) and recurrent neural network (RNN)-based encoders
and one decoder. A refiner is proposed to conduct a look-back operation inside
the model, in order to make full use of raw history trajectory information. We
evaluate SAPI on a proprietary dataset collected in real-world intersections
through autonomous vehicles. It is demonstrated that SAPI shows promising
performance when predicting vehicle trajectories at intersection, and
outperforms benchmark methods. The average displacement error(ADE) and final
displacement error(FDE) for 6-second prediction are 1.84m and 4.32m
respectively. We also show that the proposed model can accurately predict
vehicle trajectories in different scenarios
Learning Behavior Models for Interpreting and Predicting Traffic Situations
In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees
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