Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
PillarNeXt: Rethinking network designs for 3D object detection in LiDAR point clouds
In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …
object detection research focuses on designing dedicated local point aggregators for fine …
VistaGPT: Generative parallel transformers for vehicles with intelligent systems for transport automation
Diverse transport demands have resulted in the wide existence of heterogeneous vehicle
automation systems. While these systems have demonstrated effectiveness, they also pose …
automation systems. While these systems have demonstrated effectiveness, they also pose …
Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles
Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that
focuses on the development of autonomous vehicles (AVs) that are capable of interacting …
focuses on the development of autonomous vehicles (AVs) that are capable of interacting …
Distillbev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation
Abstract 3D perception based on the representations learned from multi-camera bird's-eye-
view (BEV) is trending as cameras are cost-effective for mass production in autonomous …
view (BEV) is trending as cameras are cost-effective for mass production in autonomous …
Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …
which can be collected at scale, to produce human-like behavior. However, policies based …
Fear-neuro-inspired reinforcement learning for safe autonomous driving
Ensuring safety and achieving human-level driving performance remain challenges for
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …
Rethinking imitation-based planners for autonomous driving
In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …
However, due to the absence of a standardized benchmark, the effectiveness of various …
Mixsim: A hierarchical framework for mixed reality traffic simulation
The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …