THE USE of VEHICLE DATA in ADAS DEVELOPMENT, VERIFICATION and FOLLOW-UP on the SYSTEM

Advanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews\u27 analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation

Developing a New Driver Assistance System for Overtaking on Two-Lane Roads using Predictive Models

The complexity of an overtaking maneuver on two-lane roads merits a thorough method for developing an assistance system to prevent accidents, thus reducing the number of fatalities and the associated economic costs. This research aims to introduce a new Driver Overtaking Assistance System (DOAS). This system is based on the proactive prediction of the possibility of overtaking any preceding vehicle(s) both accurately and safely. To provide a comprehensive system, different factors related to the driver, the vehicle, the road, and the environment which have an impact on the maneuver have been taken into consideration. In addition to considering the main overtaking strategies including accelerative, flying, piggybacking, and the 2+. The proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity through Hello beacon messages and a set of sensors that are used as part of the reasoning process of the context-aware architecture to safely initiate the overtaking maneuver. A classification model was implemented for both the Artificial Neural Network (ANN) and Support Vector Machine (SVM) learning algorithms. A vehicle driving simulator STISIM Drive® was used to conduct driving experiments for 100 participants of different ages, gender, and levels of mental awareness. The results obtained from the DOAS show high accuracy in aiding a safe overtaking maneuver. The classification model shows promising results in the predictions, through perfect accuracy and a very low level of outcome errors

Experimental Security Analysis of DNN-based Adaptive Cruise Control under Context-Aware Perception Attacks

Adaptive Cruise Control (ACC) is a widely used driver assistance feature for maintaining desired speed and safe distance to the leading vehicles. This paper evaluates the security of the deep neural network (DNN) based ACC systems under stealthy perception attacks that strategically inject perturbations into camera data to cause forward collisions. We present a combined knowledge-and-data-driven approach to design a context-aware strategy for the selection of the most critical times for triggering the attacks and a novel optimization-based method for the adaptive generation of image perturbations at run-time. We evaluate the effectiveness of the proposed attack using an actual driving dataset and a realistic simulation platform with the control software from a production ACC system and a physical-world driving simulator while considering interventions by the driver and safety features such as Automatic Emergency Braking (AEB) and Forward Collision Warning (FCW). Experimental results show that the proposed attack achieves 142.9x higher success rate in causing accidents than random attacks and is mitigated 89.6% less by the safety features while being stealthy and robust to real-world factors and dynamic changes in the environment. This study provides insights into the role of human operators and basic safety interventions in preventing attacks.Comment: 18 pages, 14 figures, 8 table

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

CASPNet++: Joint Multi-Agent Motion Prediction

The prediction of road users' future motion is a critical task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) in enabling the planning and execution of safe driving maneuvers. Based on our previous work, Context-Aware Scene Prediction Network (CASPNet), an improved system, CASPNet++, is proposed. In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy. Moreover, an instance-based output head is introduced to provide multi-modal trajectories for agents of interest. In extensive quantitative and qualitative analysis, we demonstrate the scalability of CASPNet++ in utilizing and fusing diverse environmental input sources such as HD maps, Radar detection, and Lidar segmentation. Tested on the urban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-art performance. The model has been deployed in a testing vehicle, running in real-time with moderate computational resources.Comment: 8 pages, 6 figure

What drives the Acceptability of Intelligent Speed Assistance (ISA)? Modeling acceptability of ISA

6370 individuals responded in Belgium (Flanders region) and 1158 persons in The Netherlands on a web-survey about ISA. A model has been estimated, by using SEM, to find out which predefined indicators would be relevant to define the acceptability of ISA. Background factors, contextual issues, and ISA-device related factors were used as indicators to predict the level of acceptability. The factors that were used in the model were based on the methods used in past ISA trials, acceptance and accep-tability theories and models. The effectiveness of ISA (1), equity (2), effectiveness of ITS (3) and personal and social aims (4), were the four variables that had the largest total effect on the acceptability of ISA. Effectiveness was found a relevant predictor for acceptance in many trials (Morsink et al, 2006). The model showed that the willingness of drivers to adopt ISA increases if they experience the system in practice: if people are convinced that ISA will assist to maintain the legal speed in different speed zones, the acceptance will be higher (Van der Pas et al., 2008). Hence, trials seem a good way to demonstrate the effectiveness of ISA. However, trials typically do not allow many people to try out ISA. Therefore, communi-cation strategies that focus on the ISA-effectiveness would be helpful to convince people about the benefits of using such a system. Often when new driver support technologies are intro-duced – especially when it could restrict certain freedom in driving – a majority of the population is reluctant when it comes to ‘buy or use’ the system. In some studies (see Morsink et al., 2006; Marchau et al., 2010) the willingness to pay was reported to be a good predictor for acceptability. However, in the present study the effect of willing-ness to pay was very low or even absent; hence it may be as-sumed that better indicators are put in the model than the willing-ness to pay. With respect to context indicators, ‘personal and social aims’ seemed to be the variable with the highest influence on accep-tability. Drivers, who rate social aims above personal aims with respect to speed and speeding, will accept ISA more. Personal and social aims had also a high influence on most of the device spe-cific indicators. Furthermore, drivers who speed for their personal benefit were found to rather speed more often. Drivers who speed in high-speed zones would also be less inclined to accept ISA. This is in line with previous findings (e.g. Jamson et al., 2006), frequent speeders would support ISA less; those drivers who would benefit most of ISA would be less likely to use it. This is an important finding when considering the strategies for implementing ISA. Some studies (e.g. Morsink et al., 2006) indicated that to increase the acceptability, implementation strategies and campaigns could focus on other benefits of ISA (like reducing speeding tickets, emissions etc.). According to our study these secondary effects have rather small effects to increase acceptability. Drivers who like to speed would even care less for these secondary benefits of ISA. The youngest group of drivers (<25 years old) would influence responsibility awareness negatively. These younger drivers are also less convinced that certain behaviour or circumstances could cause accidents. Many studies indicated that young drivers over-estimate their own driving skills, drive faster and are less aware of accident causes (Shinar et al., 2001). For the implementation of ISA – although there is no direct relationship between younger age and acceptability – a different strategy is needed to convince this group of drivers. Awareness campaigns and communication should be deployed during their education, however, road safety education and training stops during secondary school or higher education (OECD, 2006). Drivers between 25 and 45 years old would also be less inclined to accept ISA, mainly considered out of indirect effects in the esti-mated model. This group of drivers may be labelled as one of the most active groups of drivers. Another aspect is that both of the "significant found age groups were influenced by social norms. This may be very important in implementation strate-gies. For instance, role models could be used in ISA driving. This strategy was also used in the Belgian trial to gain more publicity and attention. The positive image and the improved information communication of ISA as a possible measure in road-safety have led to several voted resolutions in the Belgian federal parliament and senate (Vlassenroot et al. 2007)

Driving automation: Learning from aviation about design philosophies

Full vehicle automation is predicted to be on British roads by 2030 (Walker et al., 2001). However, experience in aviation gives us some cause for concern for the 'drive-by-wire' car (Stanton and Marsden, 1996). Two different philosophies have emerged in aviation for dealing with the human factor: hard vs. soft automation, depending on whether the computer or the pilot has ultimate authority (Hughes and Dornheim, 1995). This paper speculates whether hard or soft automation provides the best solution for road vehicles, and considers an alternative design philosophy in vehicles of the future based on coordination and cooperation

A first approach to understanding and measuring naturalness in driver-car interaction

With technology changing the nature of the driving task, qualitative methods can help designers understand and measure driver-car interaction naturalness. Fifteen drivers were interviewed at length in their own parked cars using ethnographically-inspired questions probing issues of interaction salience, expectation, feelings, desires and meanings. Thematic analysis and content analysis found five distinct components relating to 'rich physical' aspects of natural feeling interaction typified by richer physical, analogue, tactile styles of interaction and control. Further components relate to humanlike, intelligent, assistive, socially-aware 'perceived behaviours' of the car. The advantages and challenges of a naturalness-based approach are discussed and ten cognitive component constructs of driver-car naturalness are proposed. These may eventually be applied as a checklist in automotive interaction design.This research was fully funded by a research grant from Jaguar Land Rover, and partially funded by project n.220050/F11 granted by Research Council of Norway