Collaborative Analysis Framework of Safety and Security for Autonomous Vehicles

Human error has been statistically proven to be the primary cause of road accidents. This undoubtedly is a contributory cause of the rising popularity of autonomous vehicles as they are presumably able to maneuver appropriately/optimally on the roads while diminishing the likelihood of human error and its repercussion. However, autonomous vehicles are not ready for widespread adoption because their safety and security issues are yet to be thoroughly investigated/addressed. Little literature could be found on collaborative analysis of safety and security of autonomous vehicles. This paper proposes a framework for analyzing both safety and security issues, which includes an integrated safety and security method (S&S) with international vehicle safety and security standards ISO 26262 and SAE J3061. The applicability of the proposed framework is demonstrated using an example of typical autonomous vehicle model. Using this framework, one can clearly understand the vehicle functions, structure, the associated failures and attacks, and also see the vulnerabilities that are not yet addressed by countermeasures, which helps to improve the in-vehicle safety and security from researching and engineering perspectives

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

Communicating Intent in Autonomous Vehicles

abstract: The prospects of commercially available autonomous vehicles are surely tantalizing, however the implementation of these vehicles and their strain on the social dynamics between motorists and pedestrians remains unknown. Questions concerning how autonomous vehicles will communicate safety and intent to pedestrians remain largely unanswered. This study examines the efficacy of various proposed technologies for bridging the communication gap between self-driving cars and pedestrians. Displays utilizing words like “safe” and “danger” seem to be effective in communicating with pedestrians and other road users. Future research should attempt to study different external notification interfaces in real-life settings to more accurately gauge pedestrian responses.Dissertation/ThesisMasters Thesis Engineering 201

Synchronous Roundabouts with Rotating Priority Sectors (SYROPS): high capacity and safety for conventional and autonomous vehicles

Roundabouts are a highway engineering concept meant to reduce congestion and improve safety. However, experience shows that capacity of roundabouts is limited, and safety is not optimal. However, these improvements in capacity and safety should be compatible with both manually-driven and autonomous vehicles. Incorporating existing advanced technologies to the signaling and control of roundabouts will undoubtedly contribute to these improvements but should not restrict this compatibility. We approach roundabouts as synchronous switches of vehicles, and propose a roundabout system (synchronous roundabouts with rotating priorities) based on vehicle platoons arriving at the roundabout at a uniform speed and within the time slot assigned to their entry, avoiding conflicts and stops. The proposed signaling system is visual for human drivers and wireless for connected and autonomous vehicles. We evaluated analytically and with simulations roundabouts of different radii for several values of the average distance between vehicles. Results show that average delays are 28.7% lower, with negligible dispersion. The capacity improvements depend on design parameters, moderate for small roundabouts, but that goes up to 70&-100% for short inter vehicular distances and medium and large roundabouts. Simulations with unbalanced traffic maintained the capacity improvement over standard roundabouts.Comunidad de Madri

R^2IM: Reliable and Robust Intersection Manager Robust to Rogue Vehicles

abstract: At modern-day intersections, traffic lights and stop signs assist human drivers to cross the intersection safely. Traffic congestion in urban road networks is a costly problem that affects all major cities. Efficiently operating intersections is largely dependent on accuracy and precision of human drivers, engendering a lingering uncertainty of attaining safety and high throughput. To improve the efficiency of the existing traffic network and mitigate the effects of human error in the intersection, many studies have proposed autonomous, intelligent transportation systems. These studies often involve utilizing connected autonomous vehicles, implementing a supervisory system, or both. Implementing a supervisory system is relatively more popular due to the security concerns of vehicle-to-vehicle communication. Even though supervisory systems are a step in the right direction for security, many supervisory systems’ safe operation solely relies on the promise of connected data being correct, making system reliability difficult to achieve. To increase fault-tolerance and decrease the effects of position uncertainty, this thesis proposes the Reliable and Robust Intersection Manager, a supervisory system that uses a separate surveillance system to dependably detect vehicles present in the intersection in order to create data redundancy for more accurate scheduling of connected autonomous vehicles. Adding the Surveillance System ensures that the temporal safety buffers between arrival times of connected autonomous vehicles are maintained. This guarantees that connected autonomous vehicles can traverse the intersection safely in the event of large vehicle controller error, a single rogue car entering the intersection, or a sybil attack. To test the proposed system given these fault-models, MATLAB® was used to create simulations in order to observe the functionality of R2IM compared to the state-of-the-art supervisory system, Robust Intersection Manager. Though R2IM is less efficient than the Robust Intersection Manager, it considers more fault models. The Robust Intersection Manager failed to maintain safety in the event of large vehicle controller errors and rogue cars, however R2IM resulted in zero collisions.Dissertation/ThesisMasters Thesis Computer Engineering 201

An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Testing of autonomous vehicles involves enormous challenges for the automotive industry. The number of real-world driving scenarios is extremely large, and choosing effective test scenarios is essential, as well as combining simulated and real world testing. We present an industrial workbench of tools and workflows to generate efficient and effective test scenarios for active safety and autonomous driving functions. The workbench is based on existing engineering tools, and helps smoothly integrate simulated testing, with real vehicle parameters and software. We aim to validate the workbench with real cases and further refine the input model parameters and distributions

Re-inventing the journey experience - A multifaceted framework to comfort in autonomous vehicles

Future vehicles provide scope to completely re-invent the journey experience. Technological advances have enabled fast progression of driving automation which has the potential to deliver efficient, accessible, sustainable and clean transport systems. Level 4 autonomous vehicles provide an exciting opportunity for drivers and passengers to engage in many activities unrelated to the driving task (e.g. reading, work communication/social networking on mobile technologies, relaxing, watching films etc.) leading to benefits in terms of comfort, pleasure and productivity. There has already been a lot of work looking at the active safety systems autonomous vehicles will need to use as well as the accompanying Human Machine Interface (HMI). For example, studies that look at the time it takes to hand over control from the vehicle to the occupant, and from the occupant to the vehicle. However, little is known regarding the nature of the secondary activities that drivers will want to undertake, and how this will impact occupant comfort, the vehicle architecture, its features and functional safety systems. To understand the ergonomic and engineering impact, first we must capture and fully understand user needs and their preferences in terms of the type of activities that could be undertaken in-vehicle. Re-inventing the journey experience is a research program addressing the lack of research around the user experience of autonomous vehicles. The main aims of the program are to: (1) understand potential for improving the travelling experience; (2) understand what the ergonomic, legislative, safety and comfort constraints are in order to identify design constraints; (3) understand how design innovations can support new occupant requirements. This paper presents a multifaceted framework which aims to guide researchers and industry professionals to more pragmatic vehicle concepts