SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.Comment: Accepted at AAAI'2

Mountain bike suspension systems and their effect on rider performance quantified through mechanical, psychological and physiological responses

Mountain bike suspension systems have been designed to improve riding performance and comfort for the cyclist. Additionally, a suspension system may reduce fatigue, energy expenditure, and enhance time trial performance. It has also been proposed, however, that using a rear suspension system on a mountain bike may be detrimental to the cyclist, causing the cyclist’s energy to be dissipated via the rear suspension system. Prior to undertaking the current research, a survey into mountain bike suspension systems was conducted to establish rider preferences, as well as their perceptions of suspension systems and riding styles. The resulting responses - that the majority of cross-country cyclists chose to ride a bike with front suspension only (a hardtail bike), despite the significant advantages that a fully suspended system has to offer – aided in the decision to address the unanswered questions that remain in this area of research. This thesis presents an investigation into mountain bike suspension systems and their effect on rider performance, quantifying the dynamic loads exerted on the bike frame and rider. Both the psychological and physiological effects of using a rear suspension system on cross-country cycling are additional considerations of this study. An initial laboratory experiment was completed to investigate the effects of rear wheel dynamics on a rough track with a high impact frequency and the consequent impact this terrain has on rider performance, comparing a full suspension and hardtail bike. Further testing was conducted on a rolling road rig, specifically designed for the purpose of the current research, which more closely represented the conditions encountered by a cyclist on a cross-country track. Testing was conducted on the rolling road rig on both a flat road and rough track, examining the interaction forces between the bike and rider. Greater resistance was experienced by cyclists when cycling on the rolling road rig compared to the roller rig which equated to the resistance encountered when cycling uphill or into a headwind. The mechanical results from both rigs were compared to dynamic simulations as a means of validating and comparing the mechanical results. An additional series of tests was carried out on an indoor track which had a similar terrain to that of the rolling road rig. This set of tests placed fewer restrictions on the cyclist as only physiological data was collected using unobtrusive portable measurement devices, and provided further results to illuminate correlations or discrepancies between the roller rig and rolling road rig experiments. The experimental rolling road rig results indicated that, when cycling on a smooth surface, the hardtail bike offered no significant physiological advantage to the cyclist; however, more power was required by the rider to pedal the fully suspended bike. This was also advocated by the simulation results. Conversely, it was highlighted that the fully suspended bike provided a significant advantage to the rider compared to the hardtail bike when cycling on extremely rough terrain on the roller rig. This was the case across the simulation results, mechanical measurements, physiological measurements and psychological measurements. Similarly, the indoor track tests indicated that cycling on a fully suspended bike provided significant advantages to a cyclist in terms of rider performance. On the contrary, the experimental rolling road rig results on a rough surface demonstrated that no significant difference was apparent between cycling on either the hardtail or fully suspended bike. This result suggests that, when a rider encounters added resistance to cycling, as is the case when cycling uphill, there is less of an advantage for a fully suspended bike even on rough terrain

Biorefarmeries: Milking ethanol from algae for the mobility of tomorrow

The idea of this project is to fully exploit microalgae to the best of its potential, possibly proposing a sort of fourth generation fuel based on a continuous milking of macro- and microorganisms (as cows in a milk farm), which produce fuel by photosynthetic reactions. This project proposes a new transportation concept supported by a new socio-economic approach, in which biofuel production is based on biorefarmeries delivering fourth generation fuels which also have decarbonization capabilities, potential negative CO2 emissions plus positive impacts on mobility, the automotive Industry, health and environment and the econom

Bicyclist Longitudinal Motion Modeling

69A43551747123Bike is a promising, human-powered, and emission-free transportation mode that is being increasingly advocated as a sustainable mode of transportation due to its significant positive impacts on congestion and the environment. Cities in the United States have experienced a rapid increase in bicycle ridership over the past decade. However, despite the growing popularity of bicycles for short-distance commuting and even for mid-distance recreational trips, researchers have generally ignored the investigation of bicycle traffic flow dynamics. Due to the shared space and frequent interactions among heterogeneous road users, bicycle flow dynamics should be evaluated to determine the tendency of lateral dispersion and its effects on traffic efficiency and safety. Therefore, this research effort proposes to model bicyclist longitudinal motion while accounting for bicycle interactions using vehicular traffic flow techniques. From the comparison of different states of motion for these two transport modes, the authors assumed there is no major difference between vehicular and bicyclist traffic characteristics. The study revamps the Fadhloun-Rakha car-following model previously developed by the research team to make it representative of bicycle traffic flow dynamics. The possibility of capturing cyclists\u2019 behaviors through revamping certain aspects of existing car-following models is investigated. Accordingly, 33 participants were recruited to ride the bike simulator and drive the car simulator simultaneously. The participants were recruited to operate a bike-simulator in order to test the proposed model under realistic traffic conditions and verify the output of the proposed model formulation remains valid when bicyclists are operating under realistic traffic conditions. Both simulators were integrated together, and each participant could inform about the location of another participant in the simulation interval. Six scenarios based on the initial position of the bike and car were developed. Based on the collected data, the Fadhloun-Rakha model was validated to ensure the development of a good descriptor for speed and acceleration and deceleration behaviors. A reliable sample including 100 model parameters values was selected. Root Mean Square Error (RMSE) for the mentioned sample was obtained, and the smallest RMSE in each scenario was identified. Using the obtained RMSEs, the speed and acceleration trajectories for the smallest RMSE in each scenario were drawn. Eventually, the optimal values of the model parameters (a,b,d) in each scenario were specified

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change