Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash

Kohli, Puneet; Chadha, Anjali

doi:10.1007/978-3-030-12388-8_19

Cited by 52 publications

(28 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tesla vehicle has already caused fatalities on a straight road with good visibility and in a good weather [41]. On March 2018, an Uber autonomous vehicle struck and killed a pedestrian crossing the road in Arizona, USA [42]. e Uber test vehicle failed to detect the pedestrian in the environment of low visibility and failed to conduct any corresponding actions.…”

Section: Consequences Of the Attacksmentioning

confidence: 99%

Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

Meng

2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.

show abstract

Section: Consequences Of the Attacksmentioning

confidence: 99%

Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

Meng

2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…The recent trends in self-driving cars have encouraged researchers to use several object detection algorithms that include various areas in self-driving cars, such as pedestrian detection (see Figure 1) [12][13][14][15], lane detection, traffic signal detection [16], and many more. Due to the recent development in CNN and its outstanding performance in these state-of-the-art visual recognition solutions, these processes have become increasingly intensive.…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, many object detection algorithms are available, for instance, SSD (Single Shot Multibox Detector) [14][15][16][17] YOLO (You Only Look Once) [18][19][20][21], R-CNN [10], and Fast R-CNN [22,23]. All object detection models localize objects by using bounding boxes and classifying them by labels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

2021

View full text Add to dashboard Cite

Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

show abstract

“…and lidar. Many semi-autonomous cars (levels 2-3 for SAE automated vehicle classification) produced by several automotive companies (Tesla, Daimler, General Motors, and Hyundai Motor Company [1]-[4], and the many startup companies [5], [6]) can recognize local routes based on environmental characteristics such as lanes, guide rails, and preceding vehicles. This method is similar to the principle of human local route creation for normal driving, so the autonomous car does not need a precise map or localization, and instead only needs the topological map that is used by navigation systems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Local Route Generation Combining Perception and a Precise Map for Autonomous Cars

Lee

Lim

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Autonomous cars are currently widely studied in the automotive and robotics industries because autonomous driving can satisfy the needs of human drivers regarding safety, efficiency, and comfortable driving. Behavior and motion planning for an autonomous car is one of the main requirements of autonomous driving. For autonomous cars, a local route along with its road geometry and attributes of the area is required. There are two ways to obtain the local route: perception-based local route (PBLR) and precise map-based local route (MBLR) methods. First, this paper analyzes the characteristic of both the PBLR and MBLR inference methods. Then based on this analysis, this paper proposes a hybrid local route generation algorithm that chooses the best local route between the PBLR and MBLR options, according to the perceived performance and the precise map availability. To effectively create an expensive precise map, a mapping region classification algorithm is presented to selectively choose the mapping area, where the precise map must be constructed for the MBLR inference. The hybrid local route generation algorithm with the mapping region classification allows the area used for autonomous driving to be extended while reducing the cost due to the precise map. The advantages of the proposed algorithm were verified with experiments in real traffic conditions.INDEX TERMS Hybrid local route (HLR), perception-based local route (PBLR), precise map-based local route (MBLR), precise map region classification, autonomous cars.

show abstract

Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash

Cited by 52 publications

References 76 publications

Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Hybrid Local Route Generation Combining Perception and a Precise Map for Autonomous Cars

Contact Info

Product

Resources

About