Mp3: A unified model to map, perceive, predict and plan
High-definition maps (HD maps) are a key component of most modern self-driving systems
due to their valuable semantic and geometric information. Unfortunately, building HD maps …
due to their valuable semantic and geometric information. Unfortunately, building HD maps …
Perceive, predict, and plan: Safe motion planning through interpretable semantic representations
In this paper we propose a novel end-to-end learnable network that performs joint
perception, prediction and motion planning for self-driving vehicles and produces …
perception, prediction and motion planning for self-driving vehicles and produces …
Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey
Autonomous vehicle (AV) is expected to reshape the future transportation system, and its
decision-making is one of the most critical modules. Many current decision-making modules …
decision-making is one of the most critical modules. Many current decision-making modules …
Lookout: Diverse multi-future prediction and planning for self-driving
In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …
Jointly learnable behavior and trajectory planning for self-driving vehicles
The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …
Deductive reinforcement learning for visual autonomous urban driving navigation
Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
Baidu apollo auto-calibration system-an industry-level data-driven and learning based vehicle longitude dynamic calibrating algorithm
For any autonomous driving vehicle, control module determines its road performance and
safety, ie its precision and stability should stay within a carefully-designed range …
safety, ie its precision and stability should stay within a carefully-designed range …
Application of Baidu Apollo open platform in a course of control simulation experiments
M Feng, H Zhang - Computer Applications in Engineering …, 2022 - Wiley Online Library
In this paper, we introduce the application of Baidu Apollo open platform in a course of
control simulation experiments. First of all, to let students have a full understanding of …
control simulation experiments. First of all, to let students have a full understanding of …
Uncertainty-aware human-like driving policy learning with deep Bayesian inverse reinforcement learning
D Zeng, L Zheng, X Yang, Y Li - Transportmetrica A: Transport …, 2024 - Taylor & Francis
The application of deep reinforcement learning in driving policy learning for automated
vehicles is limited by the difficulty of designing reward functions. Most existing inverse …
vehicles is limited by the difficulty of designing reward functions. Most existing inverse …
Autonomous driving vehicle control auto-calibration system: An industry-level, data-driven and learning-based vehicle longitudinal dynamic calibrating algorithm
F Zhu, X Xu, L Ma, D Guo, X Cui… - 2020 IEEE Intelligent …, 2020 - ieeexplore.ieee.org
The control module is a crucial part for autonomous driving systems, a typical control
algorithm often requires vehicle dynamics (such as longitudinal dynamics) as inputs, which …
algorithm often requires vehicle dynamics (such as longitudinal dynamics) as inputs, which …