Towards Safe Autonomous Driving: Challenges of Pedestrian Detection in Rain with Automotive Radar

Steinhauser, Dagmar; Held, Patrick; Thoresz, Bernhard; Brandmeier, Thomas

doi:10.1109/eurad48048.2021.00110

Cited by 16 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To represent this ellipse graphically, the Mahalanobis transform eliminates the correlation between variables and normalizes each variable by its variance. erefore, an ellipse can be constructed from the transformation of the unit circle, according to the inverse Mahalanobis transformation (9).…”

Section: Lemma 1 An Ellipse Can Be Constructed From a Transformation ...mentioning

confidence: 99%

See 1 more Smart Citation

A V2P Collision Risk Warning Method based on LSTM in IOV

Pan

Jie

Zhang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

With the evolution of communication networks, the Internet of Vehicles (IOV) continues to accelerate the safe and rapid development of autonomous vehicles. Vehicle-to-Pedestrian (V2P) communication is a key technology in autonomous vehicles and a potential solution to realize collaborative intelligence between vehicles and pedestrians. However, the existing V2P communication early warning system does not consider the uncertainty of pedestrian trajectory, and the determination of the collision area is limited to a single point, resulting in an inaccurate system judgment and limited improvement of traffic efficiency. This paper designs a new autonomous-oriented V2P communication network architecture and completes a V2P communication early warning system based on Long Range (LoRa). A V2P anticollision model is established, and a new V2P collision risk early warning method is proposed. In this method, danger index is introduced into the early warning of collision between pedestrian and vehicle. The long short-term memory (LSTM) artificial neural network is used to predict the pedestrian’s trajectory, so as to deduce the pedestrian-vehicle collision risk area when the pedestrian trajectory is uncertain. Meanwhile, the confidence probability is used to judge whether the pedestrian and vehicle are warned. The simulation shows that the V2P collision risk warning method proposed in this paper has good performance, which can accurately warn the pedestrian and vehicle under different vehicle speeds and Global Positioning System (GPS) positioning errors. At the same time, it reflects the characteristics of intelligence brought by using LSTM methods. Using the V2P communication early warning system based on LoRa to verify the experimental results show that when the GPS positioning accuracy is submeter level, the prediction accuracy is greater than 98%. The results of the proposed method show good performance and high detection rate.

show abstract

Section: Lemma 1 An Ellipse Can Be Constructed From a Transformation ...mentioning

confidence: 99%

“…e protection of VRUs is a common topic in V2P [8]. Advanced Driver Assistance System (ADAS) uses sensor technology [9][10][11], the far-infrared method [12], computer vision [13], and a combination of methods [14] to detect pedestrian location information.…”

Section: Introductionmentioning

confidence: 99%

A V2P Collision Risk Warning Method based on LSTM in IOV

Pan

Jie

Zhang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Some other efforts have also been made on restoring or improving the performance of ADS' basic functions like human/pedestrian detection and vehicle tracking. Recognition of the particular micro-Doppler spectra [293] and multi-layer deep learning approaches [292] are used in pedestrian detection tasks in bad weather. Thermal datasets specifically targeting pedestrian [294] or large scale simulation dataset [291] are also being established to make sure that ADS can complete this essential job with the interruption of weather.…”

Section: Othersmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

show abstract

“…In complicated traffic scenarios, these approaches will be even more challenging. Further difficulties such as weather influences, typically a strength of radar systems compared with other sensors, are also the topic of current research [15].…”

mentioning

confidence: 99%

Expanding the Capabilities of Automotive Radar for Bicycle Detection With Harmonic RFID Tags at 79/158 GHz

Braun

Schoepfel

Kwiatkowski

et al. 2023

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

Although traffic safety continues to improve overall, fatalities of vulnerable traffic participants and pedal cyclists specifically have reached 30-year highs. While automotive radar sensors continue to advance and are integrated in more and more vehicles, they are not ideally suited to detect pedal cyclists, especially in complicated traffic scenarios. To mitigate the problem of the lower radar-cross section (RCS) of the cyclist, equipping them with harmonic radio frequency identification (RFID) tags is proposed. The presented system showcases the benefits of this approach by being able to conduct conventional automotive radar measurements and detect tags simultaneously. In this work, we present the printed-circuit boards (PCBs) and the necessary chipset, while especially focusing on the design of a power-efficient harmonic RFID tag. By improving the gain per current of an amplifier chain, the tag enables a range sufficient for urban scenarios while consuming little enough current to be powered by battery-operated lights. This enables the detection of pedal cyclists even in complicated scenarios at a distance of up to 80 m.

show abstract

Towards Safe Autonomous Driving: Challenges of Pedestrian Detection in Rain with Automotive Radar

Cited by 16 publications

References 11 publications

A V2P Collision Risk Warning Method based on LSTM in IOV

A V2P Collision Risk Warning Method based on LSTM in IOV

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Expanding the Capabilities of Automotive Radar for Bicycle Detection With Harmonic RFID Tags at 79/158 GHz

Contact Info

Product

Resources

About