Core Concepts
A novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories for autonomous vehicles by incorporating road constraints and kinematic constraints.
Abstract
The paper proposes a novel online trajectory planner for autonomous vehicles by formulating the motion planning problem as a sequence of spatial-temporal graphs. The key aspects of the proposed approach are:
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Spatial-Temporal Graph (STG) Formulation:
- The graph incorporates the ego vehicle, surrounding actors, and virtual nodes along the road to represent road boundaries and kinematic constraints.
- The lateral and longitudinal virtual nodes are positioned based on the kinematic constraints obtained from a simple behavioral layer.
- The graph captures the interactions between the ego and actors as well as the temporal transitions from the current step to the next.
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STG Network Architecture:
- A Graph Attention Network (GAT) is used as an encoder to generate node embeddings of the STG.
- The encoded features are then processed through a Multi-Layer Perceptron (MLP) decoder to compute weights for the virtual nodes.
- The final trajectory is obtained by taking the weighted sum of the virtual node positions.
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Potential Functions:
- Personalized potential functions are proposed to train the network, addressing safety (obstacle avoidance) and maximum velocity keeping.
- The total potential is backpropagated through the network to enable learning.
The proposed planner is evaluated on three complex driving tasks: driving through traffic, merging, and taking an exit. The results demonstrate that the STG planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride compared to two baseline methods.
Stats
The paper reports the following key metrics:
Discomfort (integrated absolute jerk) for the generated trajectories
Risk score (based on obstacle potential function)
Longitudinal distance travelled
Quotes
"The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it."
"The module which is primarily responsible for planning safely the motion of the vehicle through traffic is the trajectory planner."