45,353 research outputs found
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving
In autonomous driving, an accurate understanding of environment, e.g., the
vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in
many driving tasks such as trajectory prediction and motion planning.
Environment information comes from high-definition (HD) map and historical
trajectories of vehicles. Due to the heterogeneity of the map data and
trajectory data, many data-driven models for trajectory prediction and motion
planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a
separate and sequential manner. However, such a manner may capture biased
interpretation of interactions, causing lower prediction and planning accuracy.
Moreover, separate extraction leads to a complicated model structure and hence
the overall efficiency and scalability are sacrificed. To address the above
issues, we propose an environment representation, Temporal Occupancy Flow Graph
(TOFG). Specifically, the occupancy flow-based representation unifies the map
information and vehicle trajectories into a homogeneous data format and enables
a consistent prediction. The temporal dependencies among vehicles can help
capture the change of occupancy flow timely to further promote model
performance. To demonstrate that TOFG is capable of simplifying the model
architecture, we incorporate TOFG with a simple graph attention (GAT) based
neural network and propose TOFG-GAT, which can be used for both trajectory
prediction and motion planning. Experiment results show that TOFG-GAT achieves
better or competitive performance than all the SOTA baselines with less
training time.Comment: Accepted by ICRA 202
Data-driven battery aging diagnostics and prognostics
Lithium-ion (Li-ion) batteries play a pivotal role in transforming the transportation sector from heavily relying on fossil fuels to a low-carbon solution. But, as an electrochemical device, a battery will inevitably undergo irreversible degradation over time. Therefore, accurate and reliable aging diagnostics and prognostics become indispensable for safe and efficient battery usage during operation. However, diverse aging mechanisms, stochastic usage patterns, and cell-to-cell variations impose significant challenges. With the ever-increasing awareness of the importance of vehicle operating data, more and more automotive companies have started to collect field data. Meanwhile, the rapid advancement in computational power has drawn tremendous attention to using machine learning algorithms to solve complex and challenging tasks. In this thesis, recent data-driven modeling techniques, using both field data collected during vehicle operation and laboratory cycling data, are applied to improve the overall performance of battery aging diagnostics and prognostics. A series of data-driven methods are proposed ranging from battery state of health estimation, future aging trajectory prediction, and remaining useful life prediction. The algorithms are extensively evaluated with various data sources of different battery kinds. The evaluation results indicate that the developed methods are accurate and robust, but more importantly, they are applicable to the harsh conditions encountered in real-world vehicle operations
AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
We present AutonoVi:, a novel algorithm for autonomous vehicle navigation
that supports dynamic maneuvers and satisfies traffic constraints and norms.
Our approach is based on optimization-based maneuver planning that supports
dynamic lane-changes, swerving, and braking in all traffic scenarios and guides
the vehicle to its goal position. We take into account various traffic
constraints, including collision avoidance with other vehicles, pedestrians,
and cyclists using control velocity obstacles. We use a data-driven approach to
model the vehicle dynamics for control and collision avoidance. Furthermore,
our trajectory computation algorithm takes into account traffic rules and
behaviors, such as stopping at intersections and stoplights, based on an
arc-spline representation. We have evaluated our algorithm in a simulated
environment and tested its interactive performance in urban and highway driving
scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios
include jaywalking pedestrians, sudden stops from high speeds, safely passing
cyclists, a vehicle suddenly swerving into the roadway, and high-density
traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure
Recommended from our members
Chapter 2Â -Â Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment
- …