TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving

by   Zihao Wen, et al.

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.


page 1

page 2

page 3

page 6


A Data Driven Approach for Motion Planning of Autonomous Driving Under Complex Scenario

To guarantee the safe and efficient motion planning of autonomous drivin...

Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving

Driving in a human-like manner is important for an autonomous vehicle to...

Baidu Apollo EM Motion Planner

In this manuscript, we introduce a real-time motion planning system base...

Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles

Motion prediction of road users in traffic scenes is critical for autono...

Spatiotemporal motion planning with combinatorial reasoning for autonomous driving

Motion planning for urban environments with numerous moving agents can b...

PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation

Predicting the future motion of traffic agents is crucial for safe and e...

This is the Way: Differential Bayesian Filtering for Agile Trajectory Synthesis

One of the main challenges in autonomous racing is to design algorithms ...

Please sign up or login with your details

Forgot password? Click here to reset