[PDF][PDF] Glad: Grounded layered autonomous driving for complex service tasks
Given the current point-to-point navigation capabilities of autonomous vehicles, researchers
are looking into complex service requests that require the vehicles to visit multiple points of …
are looking into complex service requests that require the vehicles to visit multiple points of …
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
N Karnchanachari, D Geromichalos, KS Tan… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …
Occupancy prediction-guided neural planner for autonomous driving
Forecasting the scalable future states of surrounding traffic participants in complex traffic
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …
Multi-task long-range urban driving based on hierarchical planning and reinforcement learning
Multi-task long-range autonomous driving in urban areas is a challenging task. Traditional
methods are not applicable to situations with high-dimensional observations. Current …
methods are not applicable to situations with high-dimensional observations. Current …
Learning interaction-aware guidance policies for motion planning in dense traffic scenarios
B Brito, A Agarwal, J Alonso-Mora - arXiv preprint arXiv:2107.04538, 2021 - arxiv.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Planning-oriented autonomous driving
Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
Learning urban navigation via value iteration network
S Yang, J Li, J Wang, Z Liu… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
Choosing an appropriate route is a critical problem in urban navigation. Being familiar with
roads topology and other vehicles' routes, experienced drivers could usually find a near …
roads topology and other vehicles' routes, experienced drivers could usually find a near …
Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
W Zhang, M Elmahgiubi, K Rezaee… - arXiv preprint arXiv …, 2024 - arxiv.org
In this paper we present the architecture of the Kyber-E2E submission to the map track of
CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first …
CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first …
Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios
B Brito, A Agarwal… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Exploring data aggregation in policy learning for vision-based urban autonomous driving
Data aggregation techniques can significantly improve vision-based policy learning within a
training environment, eg, learning to drive in a specific simulation condition. However, as on …
training environment, eg, learning to drive in a specific simulation condition. However, as on …