RoadView: A traffic scene simulator for autonomous vehicle simulation testing

Zhang, Chi; Liu, Yuehu; Zhao, Dongfang; Su, Yuanqi

doi:10.1109/itsc.2014.6957844

Cited by 45 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the number of objects, it will set a guideline to calculate its possible future trajectory. For this it will make use of a set of sensors (such as LiDAR, cameras, radar, and Global Positioning System) that will allow it to detect objects, their position, their distance and keep track of objects (moving and stationaries) [35].…”

Section: Cavs-vru Interaction Processmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

View full text Add to dashboard Cite

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

show abstract

Section: Cavs-vru Interaction Processmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…There are many other simulators available that are not explicitly reviewed in this paper. For example, RoadView is a traffic scene modelling simulator built using image sequences and the road Global Information System (GIS) data [46]. [6] provides an in-depth review of CARLA simulator and how it can be used to test autonomous driving algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Simulators for Testing Self-Driving Cars

Kaur

Taghavi

Tian

et al. 2021

2021 Fourth International Conference on Connected and Autonomous Driving (MetroCAD)

View full text Add to dashboard Cite

A rigorous and comprehensive testing plays a key role in training self-driving cars to handle variety of situations that they are expected to see on public roads.The physical testing on public roads is unsafe, costly, and not always reproducible. This is where testing in simulation helps fill the gap, however, the problem with simulation testing is that it is only as good as the simulator used for testing and how representative the simulated scenarios are of the real environment. In this paper, we identify key requirements that a good simulator must have. Further, we provide a comparison of commonly used simulators. Our analysis shows that CARLA and LGSVL simulators are the current state-of-theart simulators for end to end testing of self-driving cars for the reasons mentioned in this paper. Finally, we also present current challenges that simulation testing continues to face as we march towards building fully autonomous cars.

show abstract

“…e number of all positive samples predicted by the model is TP + FP, and the proportion of correct positive samples is called precision, as shown in formula (5). e number of all positive samples in the validation set is TP + FN, and the proportion of predicted positive samples is called recall, as shown in formula (6).…”

Section: Performance Evaluation Of Proposed Workmentioning

confidence: 99%

“…The main task of autonomous driving is to accurately and quickly detect the vehicles, pedestrians, traffic lights, traffic signs, and other objects around the vehicles, in order to ensure the safety in driving. Generally, autonomous vehicles use various sensors, such as cameras, lidar, and radar, to detect objects [ 5 ]. Some researchers [ 6 ] detect vehicles by extracting binary images from discrete sensor arrays, and some researchers [ 7 ] have achieved good results in the detection task in bad weather through the sensing method of radar and camera information fusion.…”

Section: Introductionmentioning

confidence: 99%

A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

Wang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

show abstract

RoadView: A traffic scene simulator for autonomous vehicle simulation testing

Cited by 45 publications

References 9 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

A Survey on Simulators for Testing Self-Driving Cars

A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

Contact Info

Product

Resources

About