ACP-incorporated perturbation-resistant neural dynamics controller for autonomous vehicles
Autonomous vehicle control systems are unavoidably influenced by diverse noise
perturbations from the unpredictable external environment and internal system. In this …
perturbations from the unpredictable external environment and internal system. In this …
MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding
road users' future behavior. In response to this need and the associated challenges, we …
road users' future behavior. In response to this need and the associated challenges, we …
A faster cooperative lane change controller enabled by formulating in spatial domain
Lane-Change (LC) maneuvers are deemed to jeopardize traffic safety, mobility, and
sustainability. Cooperative Lane-Change (CLC) solves this problem by accelerating the LC …
sustainability. Cooperative Lane-Change (CLC) solves this problem by accelerating the LC …
Parallel transportation in TransVerse: From foundation models to DeCAST
Rapid development of AI technologies has propelled the seamless integration of physical
and cyber worlds with various kinds of online/offline information collected from millions of …
and cyber worlds with various kinds of online/offline information collected from millions of …
Interaction-aware motion planning for autonomous vehicles with multi-modal obstacle uncertainty predictions
This article proposes an interaction and safety-aware motion-planning method for an
autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates …
autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates …
Integrated decision making and planning based on feasible region construction for autonomous vehicles considering prediction uncertainty
L Xiong, Y Zhang, Y Liu, H Xiao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
For autonomous vehicles, scene understanding is still one of the major challenges, which
needs to be well handled to avoid jittery decisions and unsmooth trajectories. Furthermore …
needs to be well handled to avoid jittery decisions and unsmooth trajectories. Furthermore …
Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
The application of deep reinforcement learning (DRL) techniques in intelligent transportation
systems garners significant attention. In this field, reward function design is a crucial factor …
systems garners significant attention. In this field, reward function design is a crucial factor …
Interaction and decision making-aware motion planning using branch model predictive control
Motion planning for autonomous vehicles sharing the road with human drivers remains
challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi …
challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi …
Safety-Dominant Stochastic Model Predictive Decision-Making Considering Obstacle Trajectory Uncertainties for Intelligent Vehicles
Robotaxis tend to hesitate when encountering obstacles with uncertain future trajectories
because their decision-making strategies are overly cautious, aiming to ensure robust …
because their decision-making strategies are overly cautious, aiming to ensure robust …
RACP: Risk-Aware Contingency Planning with Multi-Modal Predictions
For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is
imperative to assess the repercussions of its prospective actions by anticipating the …
imperative to assess the repercussions of its prospective actions by anticipating the …