activity
20152023
most citedPedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

147 citations · 590 across the 30 of their papers we have counts for

collaborators

32 papers

cs.RO2023★ 3 cited

Formal Verification of Robotic Contact Tasks via Reachability Analysis

Chencheng Tang, Matthias Althoff

Verifying the correct behavior of robots in contact tasks is challenging due to model uncertainties associated with contacts. Standard methods for testing often fall short since al…

eess.SY2023★ 24 cited

Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-organization Strategies and Provably-correct Design

Di Liu, Sebastian Mair, Kang Yang +3

This work proposes provably-correct self-organizing strategies for platoons of heterogeneous vehicles. We refer to self-organization as the capability of a platoon to autonomously…

cs.RO2023★ 5 cited

Automatic Traffic Scenario Conversion from OpenSCENARIO to CommonRoad

Yuanfei Lin, Michael Ratzel, Matthias Althoff

Scenarios are a crucial element for developing, testing, and verifying autonomous driving systems. However, open-source scenarios are often formulated using different terminologies…

cs.LG2023★ 3 cited

Fully Automatic Neural Network Reduction for Formal Verification

Tobias Ladner, Matthias Althoff

Formal verification of neural networks is essential before their deployment in safety-critical applications. However, existing methods for formally verifying neural networks are no…

math.CO2023★ 1 cited

M-Representation of Polytopes

Sebastian Sigl, Matthias Althoff

We introduce the M-representation of polytopes, which makes it possible to compute linear transformations, convex hulls, and Minkowski sums with linear complexity in the dimension…

cs.LG2023

Deep Occupancy-Predictive Representations for Autonomous Driving

Eivind Meyer, Lars Frederik Peiss, Matthias Althoff

Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural mo…