Recently, autonomous vehicles (AVs) have received a lot of attention from the automotive industry as well as the AV research community across the globe. To increase the safety of the flow of traffic, they are anticipated to help or perhaps take the place of human drivers when it comes to vehicle manoeuvring at various levels of autonomy. But before they can be widely used, AVs must first be developed to overcome their inherent security and road safety issues. The adoption of autonomous vehicles depends on the results of the driving test and their safety validation on the roads. This paper examines the associated autonomous vehicle testing and validation methodologies such as autonomous vehicle functional testing, integrated vehicle testing, and system verification across many architectures. In addition, the paper presents some of the state-of-the-art machine learning algorithms used for AV operation on roads. Knowledge of such recent trends in the validation and verification techniques for road safety will be helpful for the development of autonomous vehicles.

Accelerating the performance of optimization algorithms is crucial for many day-to-day applications. Mobility-on-demand is one such application that is transforming urban mobility by offering reliable and convenient on-demand door-to-door transportation at any time. Dial-a-ride problem (DARP) is an underlying optimization problem in the operational planning of mobility-on-demand systems. The primary objective of DARP is to design routes and schedules to serve passenger transportation requests with high-level user comfort. DARP often arises in dynamic real-world scenarios, where rapid route planning is essential. The traditional CPU-based algorithms are generally too slow to be useful in practice. Since customers expect quick response for their mobility requests, there has been a growing interest in fast solution methods. Therefore, in this paper, we introduce a GPU-based solution methodology for the dial-a-ride problem to produce good solutions in a short time. Specifically, we develop a GPU framework to accelerate time-critical neighborhood exploration of local search operations under the guidance of metaheuristics such as tabu search and variable neighborhood search. Besides, we propose device-oriented optimization strategies to enhance the utilization of a current-generation GPU architecture (Tesla P100). We report speedup achieved by our GPU approach when compared to its classical CPU counterpart, and the effect of each device optimization strategy on computational speedup. Results are based on standard test instances from the literature. Ultimately, the proposed GPU methodology generates better solutions in a short time when compared to the existing sequential approaches.

Dial-a-ride problem (DARP) deals with the transportation of users between pickup and drop-off locations associated with specified time windows. This paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve the dial-a-ride problem. Two new local search operators (burn and reform), a new construction heuristic and two request sequencing mechanisms (Sorted_List and Random_List) are developed. Computational experiments conducted on various standard DARP benchmark instances prove that MATA is an expeditious meta-heuristic in contrast to other existing methods. In all experiments, MATA demonstrates the capability to obtain high quality solutions, faster convergence, and quicker attainment of a first feasible solution. It is observed that MATA attains a first feasible solution 29.8 to 65.1% faster, and obtains a final solution that is 3.9 to 5.2% better, when compared to other algorithms within 60 sec.