Eugen Šlapak
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Eugen Šlapak has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Matúš Dopiriak
Department of Computers and Informatics, Technical University of Kosice, 042 00 Kosice, Slovakia
3 shared publications - Arisha Al Faruque
Irvine Valley College, Irvine, CA 92 618, USA
2 shared publications - Gabriel Bugár
Department of Computer Networks, Technical University of Košice, 042 00 Košice, Slovakia
2 shared publications - Matúš Čavojský
Department of Computer Networks, Technical University of Košice, 042 00 Košice, Slovakia
2 shared publications - Tomáš Doboš
Department of Computers and Informatics, Technical University of Kosice, 042 00 Kosice, Slovakia
2 shared publications - Juraj Gazda
Technical University of Košice
1 shared publication
Latest Publications
- Open Access
- 5 July 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (13) , 6722
Real-Time Detection of Rare Traffic Situations Using RGB-LiDAR Fusion and a Rule-Based Safety Agent in CARLA
Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are underrepresented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA (Car Learning to Act), a reproducible simulator for AD research. The approach combines YOLOv8n-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, Time-to-Collision (TTC)-aware escalation, and an automatic emergency braking (AEB) override above the CARLA autopilot. Fused observations are classified as semantic–geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence rather than confirmed rare objects. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios comprising 19,253 frames, with additional weather/lighting stress tests and a public nuScenes mini cross-platform check. On a manually annotated subset of 4800 CARLA frames, corresponding to approximately of the recorded CARLA log, the full framework achieved precision, recall, and a F1-score for safety-relevant threat detection. The control experiments show that the fusion-based safety agent reduced unnecessary braking to compared with for the LiDAR-only baseline and achieved event-level success on the annotated critical intervals. The proposed CPU-only implementation maintained real-time performance, with an average processing time of .- 5 July 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (13) , 6722
Open AccessReal-Time Detection of Rare Traffic Situations Using RGB-LiDAR Fusion and a Rule-Based Safety Agent in CARLA
Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are underrepresented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA (Car Learning to Act), a reproducible simulator for AD research. The approach combines YOLOv8n-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, Time-to-Collision (TTC)-aware escalation, and an automatic emergency braking (AEB) override above the CARLA autopilot. Fused observations are classified as semantic–geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence rather than confirmed rare objects. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios comprising 19,253 frames, with additional weather/lighting stress tests and a public nuScenes mini cross-platform check. On a manually annotated subset of 4800 CARLA frames, corresponding to approximately of the recorded CARLA log, the full framework achieved precision, recall, and a F1-score for safety-relevant threat detection. The control experiments show that the fusion-based safety agent reduced unnecessary braking to compared with for the LiDAR-only baseline and achieved event-level success on the annotated critical intervals. The proposed CPU-only implementation maintained real-time performance, with an average processing time of . - Open Access
- 19 June 2026
- Published by MDPI AG in Preprints.org
Real-Time Detection of Rare Traffic Situations Using RGB-LiDAR Fusion and a Rule-Based Safety Agent in CARLA
Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are poorly represented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA. The approach combines YOLOv8-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, and an automatic emergency braking (AEB) override. Fused observations are classified as semantic-geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios, comprising 19253 frames. On a manually annotated subset of 1200 frames, the full pipeline achieved 93.7% precision, 94.7% recall, and a 94.2% F1-score. The CPU implementation processed one frame in 34.7 ms on average, remaining within the 50 ms budget of a 20 Hz simulation tick.
- 19 June 2026
- Published by MDPI AG in Preprints.org
Open AccessReal-Time Detection of Rare Traffic Situations Using RGB-LiDAR Fusion and a Rule-Based Safety Agent in CARLA
Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are poorly represented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA. The approach combines YOLOv8-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, and an automatic emergency braking (AEB) override. Fused observations are classified as semantic-geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios, comprising 19253 frames. On a manually annotated subset of 1200 frames, the full pipeline achieved 93.7% precision, 94.7% recall, and a 94.2% F1-score. The CPU implementation processed one frame in 34.7 ms on average, remaining within the 50 ms budget of a 20 Hz simulation tick.
- Open Access
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving
The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more. DTs can be created with 3D scene reconstruction methods that capture the real world's geometry, appearance, and dynamics. However, sending data for real-time DT updates in the metaverse, such as camera images and videos from connected autonomous vehicles (CAVs) to edge servers, can increase network congestion, costs, and latency, affecting metaverse services. Herein, a new method is proposed based on distributed radiance fields (RFs), multi-access edge computing (MEC) network for video compression and metaverse DT updates. RF-based encoder and decoder are used to create and restore representations of camera images. The method is evaluated on a dataset of camera images from the CARLA simulator. Data savings of up to 80% were achieved for H.264 I-frame - P-frame pairs by using RFs instead of I-frames, while maintaining high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) qualitative metrics for the reconstructed images. Possible uses and challenges for the metaverse and autonomous mobility are also discussed.
Open AccessDistributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving
The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more. DTs can be created with 3D scene reconstruction methods that capture the real world's geometry, appearance, and dynamics. However, sending data for real-time DT updates in the metaverse, such as camera images and videos from connected autonomous vehicles (CAVs) to edge servers, can increase network congestion, costs, and latency, affecting metaverse services. Herein, a new method is proposed based on distributed radiance fields (RFs), multi-access edge computing (MEC) network for video compression and metaverse DT updates. RF-based encoder and decoder are used to create and restore representations of camera images. The method is evaluated on a dataset of camera images from the CARLA simulator. Data savings of up to 80% were achieved for H.264 I-frame - P-frame pairs by using RFs instead of I-frames, while maintaining high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) qualitative metrics for the reconstructed images. Possible uses and challenges for the metaverse and autonomous mobility are also discussed.