Motion Prediction on Self-driving Cars: A Review

• URL: http://arxiv.org/abs/2011.03635v1
• Date: Fri, 6 Nov 2020 23:40:37 GMT
• Title: Motion Prediction on Self-driving Cars: A Review
• Authors: Shahrokh Paravarzar and Belqes Mohammad
• Abstract summary: Motion prediction is the most challenging task in autonomous vehicles and self-drive cars. Deep reinforcement learning is the best candidate to tackle self-driving cars.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The autonomous vehicle motion prediction literature is reviewed. Motion prediction is the most challenging task in autonomous vehicles and self-drive cars. These challenges have been discussed. Later on, the state-of-theart has reviewed based on the most recent literature and the current challenges are discussed. The state-of-the-art consists of classical and physical methods, deep learning networks, and reinforcement learning. prons and cons of the methods and gap of the research presented in this review. Finally, the literature surrounding object tracking and motion will be presented. As a result, deep reinforcement learning is the best candidate to tackle self-driving cars.

Related papers

• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions [2.693342141713236]
  This paper reviews publications on computer vision and autonomous driving that are published during the last ten years. In particular, we first investigate the development of autonomous driving systems and summarize these systems that are developed by the major automotive manufacturers from different countries. Then, a comprehensive overview of computer vision applications for autonomous driving such as depth estimation, object detection, lane detection, and traffic sign recognition are discussed.
  arXiv  Detail & Related papers  (2023-11-15T16:41:18Z)
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
  Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers. In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
  arXiv  Detail & Related papers  (2023-11-02T07:23:33Z)
• Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey [9.385936248154987]
  End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems. Recent developments in End-to-End autonomous driving are analyzed, and research is categorized based on underlying principles. This paper assesses the state-of-the-art, identifies challenges, and explores future possibilities.
  arXiv  Detail & Related papers  (2023-07-10T07:00:06Z)
• Data and Knowledge for Overtaking Scenarios in Autonomous Driving [0.0]
  The overtaking maneuver is one of the most critical actions of driving. Despite the amount of work available in the literature, just a few handle overtaking maneuvers. This work contributes in this area by presenting a new synthetic dataset whose focus is the overtaking maneuver.
  arXiv  Detail & Related papers  (2023-05-30T21:27:05Z)
• Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits [81.22616193933021]
  The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021. It will benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. It is an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations.
  arXiv  Detail & Related papers  (2022-02-08T11:55:05Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Explainability of vision-based autonomous driving systems: Review and challenges [33.720369945541805]
  The need for explainability is strong in driving, a safety-critical application. This survey gathers contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI)
  arXiv  Detail & Related papers  (2021-01-13T19:09:38Z)
• Learning Accurate and Human-Like Driving using Semantic Maps and Attention [152.48143666881418]
  This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. We exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data.
  arXiv  Detail & Related papers  (2020-07-10T22:25:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.