Vehicles controlled by autonomous driving software (ADS) are expected to bring many social and economic benefits, but at the current stage not being broadly used due to concerns with regard to their safety. Virtual tests, where autonomous vehicles are tested in software simulation, are common practices because they are more efficient and safer compared to field operational tests. Specifically, search-based approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, system-atically producing bug-revealing tests for ADS remains a major challenge. To address this challenge, we introduce DoppelTest, a test generation approach for ADSes that utilizes a genetic algorithm to discover bug-revealing violations by generating scenarios with multiple autonomous vehicles that account for traffic control (e.g., traffic signals and stop signs). Our extensive evaluation shows that DoppelTest can efficiently discover 123 bug-revealing violations for a production-grade ADS (Baidu Apollo) which we then classify into 8 unique bug categories.

Digital food journaling can support personal goals, such as weight loss and developing healthy eating behaviors. However, traditional manual tracking demands great effort, often leading to lapses or abandonment. We explore opportunities for journaling with multiple input modalities and devices, leveraging people's daily interactions with a range of technologies. We report on an extended analysis of 15 participants' experiences with ModEat, a prototype supporting journaling with several input modalities on phone, computer, and voice assistants. Participants' modality and device preferences were largely influenced by their goals, but they frequently deviated from those preferences depending on device availability, perceived affordances, and characteristics of foods eaten. Participants rarely combined input modalities in entries, but some described that doing so allowed for more detailed journaling or serve as a placeholder for later. We discuss advantages and drawbacks of multimodal tracking and potential strategies for improving interactions.

Autonomous Vehicles (AVs) leverage advanced sensing and networking technologies (e.g., camera, LiDAR, RADAR, GPS, DSRC, 5G, etc.) to enable safe and efficient driving without human drivers. Although still in its infancy, AV technology is becoming increasingly common and could radically transform our transportation system and by extension, our economy and society. As a result, there is tremendous global enthusiasm for research, development, and deployment of AVs, e.g., self-driving taxis and trucks from Waymo and Baidu. The current practice for testing AVs uses virtual tests—where AVs are tested in software simulations—since they offer a more efficient and safer alternative compared to field operational tests. Specifically, search-based approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, systematically creatingvalidandeffectivetests for AV software remains a major challenge. To address this challenge, we introducescenoRITA, a test generation approach for AVs that uses an evolutionary algorithm with (1) a novel gene representation that allows obstacles to befully mutable, hence, resulting in more reported violations and more diverse scenarios, (2) 5 test oracles to determine both safety and motion sickness-inducing violations and (3) a novel technique to identify and eliminate duplicate tests. Our extensive evaluation shows thatscenoRITAcan produce test scenarios that are more effective in revealing ADS bugs and more diverse in covering different parts of the map compared to other state-of-the-art test generation approaches.