[HTML][HTML] Autonomous driving architectures: insights of machine learning and deep learning algorithms
MR Bachute, JM Subhedar - Machine Learning with Applications, 2021 - Elsevier
Abstract Research in Autonomous Driving is taking momentum due to the inherent
advantages of autonomous driving systems. The main advantage being the disassociation …
advantages of autonomous driving systems. The main advantage being the disassociation …
[HTML][HTML] Autonomous vehicles enabled by the integration of IoT, edge intelligence, 5G, and blockchain
A Biswas, HC Wang - Sensors, 2023 - mdpi.com
The wave of modernization around us has put the automotive industry on the brink of a
paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily …
paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily …
M2i: From factored marginal trajectory prediction to interactive prediction
Predicting future motions of road participants is an important task for driving autonomously in
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …
Scept: Scene-consistent, policy-based trajectory predictions for planning
Trajectory prediction is a critical functionality of autonomous systems that share
environments with uncontrolled agents, one prominent example being self-driving vehicles …
environments with uncontrolled agents, one prominent example being self-driving vehicles …
Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents
In this paper, we address the problem of predicting the future motion of a dynamic agent
(called a target agent) given its current and past states as well as the information on its …
(called a target agent) given its current and past states as well as the information on its …
Diverse and admissible trajectory forecasting through multimodal context understanding
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …
Stretchbev: Stretching future instance prediction spatially and temporally
In self-driving, predicting future in terms of location and motion of all the agents around the
vehicle is a crucial requirement for planning. Recently, a new joint formulation of perception …
vehicle is a crucial requirement for planning. Recently, a new joint formulation of perception …
Social-implicit: Rethinking trajectory prediction evaluation and the effectiveness of implicit maximum likelihood estimation
Abstract Best-of-N (BoN) Average Displacement Error (ADE)/Final Displacement Error (FDE)
is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not …
is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not …
Scenario understanding and motion prediction for autonomous vehicles—review and comparison
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
Divide-and-conquer for lane-aware diverse trajectory prediction
Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …