29 citations · 121 across the 63 of their papers we have counts for
27 papers · 1 filter
Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving
Ahmed Abouelazm, Rupert Polley, Qingyuan Zhang +4
End-to-end planners based on waypoint regression achieve strong open-loop accuracy, but they primarily learn to mimic expert geometry and remain difficult to adapt to deployment-ti…
Safety-Critical Scenarios Emerge from Initial Scenes
Yin Wu, Jiarong Wei, Carl Esselborn +4
Safety-critical driving scenario generation has largely focused on manipulating the behavior of surrounding agents while starting from an initial scene from driving data. This assu…
Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving
Ahmed Abouelazm, Jonas Michel, Daniel Bogdoll +2
Autonomous driving involves multiple, often conflicting objectives such as safety, efficiency, and comfort. In reinforcement learning (RL), these objectives are typically combined…
Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
Stefan Orf, Sven Ochs, Marc René Zofka +1
Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the uti…
Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
Stefan Orf, Sven Ochs, Valentin Marotta +3
In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of ind…
Centralized Decision-Making for Platooning By Using SPaT-Driven Reference Speeds
Melih Yazgan, Süleyman Tatar, J. Marius Zöllner
This paper introduces a centralized approach for fuel-efficient urban platooning by leveraging real-time Vehicle- to-Everything (V2X) communication and Signal Phase and Timing (SPa…
Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving
Ahmed Abouelazm, Mianzhi Liu, Christian Hubschneider +3
Accurate prediction of surrounding road users' trajectories is essential for safe and efficient autonomous driving. While deep learning models have improved performance, challenges…
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
Ahmed Abouelazm, Jonas Michel, Helen Gremmelmaier +3
Reinforcement Learning (RL) is a promising approach for achieving autonomous driving due to robust decision-making capabilities. RL learns a driving policy through trial and error…
A Chefs KISS -- Utilizing semantic information in both ICP and SLAM framework
Sven Ochs, Marc Heinrich, Philip Schörner +2
For utilizing autonomous vehicle in urban areas a reliable localization is needed. Especially when HD maps are used, a precise and repeatable method has to be chosen. Therefore acc…
Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems
Stefan Orf, Sven Ochs, Jens Doll +4
Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous…
Empowering Autonomous Shuttles with Next-Generation Infrastructure
Sven Ochs, Melih Yazgan, Rupert Polley +9
As cities strive to address urban mobility challenges, combining autonomous transportation technologies with intelligent infrastructure presents an opportunity to transform how peo…
Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation
Daniel Bogdoll, Finn Sartoris, Vincent Geppert +2
Autonomous vehicles drive millions of miles on the road each year. Under such circumstances, deployed machine learning models are prone to failure both in seemingly normal situatio…
A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Ahmed Abouelazm, Jonas Michel, J. Marius Zoellner
Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives an…
CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research
Marc Heinrich, Maximilian Zipfl, Marc Uecker +9
Real world testing is of vital importance to the success of automated driving. While many players in the business design purpose build testing vehicles, we designed and build a mod…
Scene-Extrapolation: Generating Interactive Traffic Scenarios
Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner
Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verify…
One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level
Sven Ochs, Jens Doll, Daniel Grimm +15
Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. There…
Leveraging Swarm Intelligence to Drive Autonomously: A Particle Swarm Optimization based Approach to Motion Planning
Sven Ochs, Jens Doll, Marc Heinrich +4
Motion planning is an essential part of autonomous mobile platforms. A good pipeline should be modular enough to handle different vehicles, environments, and perception modules. Th…
Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors
Marc Uecker, J. Marius Zöllner
In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications. In comparison to previous method…
On The Impact of Replacing Private Cars with Autonomous Shuttles: An Agent-Based Approach
Daniel Bogdoll, Louis Karsch, Jennifer Amritzer +1
The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based sim…
Conditional Unscented Autoencoders for Trajectory Prediction
Faris Janjoš, Marcel Hallgarten, Anthony Knittel +3
The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic…
KI-PMF: Knowledge Integrated Plausible Motion Forecasting
Abhishek Vivekanandan, Ahmed Abouelazm, Philip Schörner +1
Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily conce…
Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
Faris Janjoš, Max Keller, Maxim Dolgov +1
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in…
Inverse Universal Traffic Quality -- a Criticality Metric for Crowded Urban Traffic Scenes
Barbara Schütt, Maximilian Zipfl, J. Marius Zöllner +1
An essential requirement for scenario-based testing the identification of critical scenes and their associated scenarios. However, critical scenes, such as collisions, occur compar…
A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving
Maximilian Zipfl, Nina Koch, J. Marius Zöllner
The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a pro…