67 research outputs found
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
Clustering traffic scenarios and detecting novel scenario types are required
for scenario-based testing of autonomous vehicles. These tasks benefit from
either good similarity measures or good representations for the traffic
scenarios. In this work, an expert-knowledge aided representation learning for
traffic scenarios is presented. The latent space so formed is used for
successful clustering and novel scenario type detection. Expert-knowledge is
used to define objectives that the latent representations of traffic scenarios
shall fulfill. It is presented, how the network architecture and loss is
designed from these objectives, thereby incorporating expert-knowledge. An
automatic mining strategy for traffic scenarios is presented, such that no
manual labeling is required. Results show the performance advantage compared to
baseline methods. Additionally, extensive analysis of the latent space is
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Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Due to the current developments towards autonomous driving and vehicle active
safety, there is an increasing necessity for algorithms that are able to
perform complex criticality predictions in real-time. Being able to process
multi-object traffic scenarios aids the implementation of a variety of
automotive applications such as driver assistance systems for collision
prevention and mitigation as well as fall-back systems for autonomous vehicles.
We present a fully model-based algorithm with a parallelizable architecture.
The proposed algorithm can evaluate the criticality of complex, multi-modal
(vehicles and pedestrians) traffic scenarios by simulating millions of
trajectory combinations and detecting collisions between objects. The algorithm
is able to estimate upcoming criticality at very early stages, demonstrating
its potential for vehicle safety-systems and autonomous driving applications.
An implementation on an embedded system in a test vehicle proves in a
prototypical manner the compatibility of the algorithm with the hardware
possibilities of modern cars. For a complex traffic scenario with 11 dynamic
objects, more than 86 million pose combinations are evaluated in 21 ms on the
GPU of a Drive PX~2
Gradient Derivation for Learnable Parameters in Graph Attention Networks
This work provides a comprehensive derivation of the parameter gradients for
GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
GATs have proven to be powerful frameworks for processing graph-structured data
and, hence, have been used in a range of applications. However, the achieved
performance by these attempts has been found to be inconsistent across
different datasets and the reasons for this remains an open research question.
As the gradient flow provides valuable insights into the training dynamics of
statistically learning models, this work obtains the gradients for the
trainable model parameters of GATv2. The gradient derivations supplement the
efforts of [2], where potential pitfalls of GATv2 are investigated
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