Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, which will affect the later modules in self-driving such as planning and control. Hence, uncertainty quantification is crucial for safety-critical systems such as CAVs. Our work is the first to estimate the uncertainty of collaborative object detection. We propose a novel uncertainty quantification method, called Double- M Quantification, which tailors a moving block bootstrap (MBB) algorithm with direct modeling of the multivariant Gaussian distribution of each corner of the bounding box. Our method captures both the epistemic uncertainty and aleatoric uncertainty with one inference pass based on the offline Double- M training process. And it can be used with different collaborative object detectors. Through experiments on the comprehensive collaborative perception dataset, we show that our Double-M method achieves more than 4× improvement on uncertainty score and more than 3% accuracy improvement, compared with the state-of-the-art uncertainty quantification methods. Our code is public on https://coperception.github.io/double-m-quantification/.

With the development of sensing and communication technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based methodologies are integrated into the control process of physical systems and demonstrate prominent performance in a wide array of CPS domains, such as connected autonomous vehicles (CAVs). However, it remains challenging to mathematically characterize the improvement of the performance of CAVs with communication and cooperation capability. When each individual autonomous vehicle is originally self-interest, we can not assume that all agents would cooperate naturally during the training process. In this work, we propose to reallocate the system's total reward efficiently to motivate stable cooperation among autonomous vehicles. We formally define and quantify how to reallocate the system's total reward to each agent under the proposed transferable utility game, such that communication-based cooperation among multi-agents increases the system's total reward. We prove that Shapley value-based reward reallocation of MARL locates in the core if the transferable utility game is a convex game. Hence, the cooperation is stable and efficient and the agents should stay in the coalition or the cooperating group. We then propose a cooperative policy learning algorithm with Shapley value reward reallocation. In experiments, compared with several literature algorithms, we show the improvement of the mean episode system reward of CAV systems using our proposed algorithm.

Approximate computing is an emerging strategy to improve the energy efficiency of many error-tolerant applications. To design an approximate circuit automatically, many approximate logic synthesis (ALS) methods have been proposed, among which many are greedy. To improve the synthesis quality of these greedy methods, one key is to calculate the errors of all candidate approximate transformations accurately. However, the traditional simulation-based method is time consuming. Instead, many existing methods just perform quick but inaccurate error estimation. In this work, to improve both the accuracy and runtime of error estimation, we propose VECBEE, a versatile efficiency–accuracy configurable batch error estimation method for greedy ALS. It is based on Monte Carlo simulation and an efficient technique to capture whether a signal change due to an introduced approximation will be propagated to each primary output. VECBEE is generally applicable to any statistical error measurement, such as error rate and average error magnitude, and any graph-based circuit representation. It allows a flexible tradeoff between the error estimation accuracy and the runtime, while even the fully accurate version is much faster than the traditional simulation-based method. We apply VECBEE to two representative greedy ALS methods and demonstrate its effectiveness in generating better approximate circuits. The code of VECBEE is made open source.

Recently, much attention has been paid to approximate computing, a novel design paradigm for error-tolerant applications. It can significantly reduce area, power, and delay of circuits by introducing an acceptable amount of error. In this paper, we propose a new heuristic method for two-level approximate logic synthesis. The problem is to identify an approximate sum-of-product (SOP) expression under a given error rate (ER) constraint so that it has the fewest literals. The basic idea of our method is to find an optimal set of input combinations for 0-to-1 output complement (SICC). For this purpose, we first identify all prime SICCs, which are fundamental SICCs in the sense that the optimal SICC is very likely to be a union of a subset of the prime SICCs. Then, we search among all subsets of the prime SICCs the optimal subset, which leads to a final good approximate SOP. We further propose four speed-up techniques. The experiments on benchmarks showed that our method is better than the previous state-of-the-art method and our speed-up techniques are effective. For an ER threshold of 0.8%, our method can reduce 15.8% literals on average.

Approximate computing is an emerging energy-efficient paradigm for error-resilient applications. Approximate logic synthesis (ALS) is an important field of it. To improve the existing ALS flows, one key issue is to derive a more accurate and efficient batch error estimation technique for all approximate transformations under consideration. In this work, we propose a novel batch error estimation method based on Monte Carlo simulation and local change propagation. It is generally applicable to any statistical error measurement such as error rate and average error magnitude. We applied the technique to an existing state-of-the-art ALS approach and demonstrated its effectiveness in deriving better approximate circuits.

Approximate computing is an emerging paradigm for error-tolerant applications. By introducing a reasonable amount of inaccuracy, both the area and delay of a circuit can be reduced significantly. To synthesize approximate circuits automatically, many approximate logic synthesis (ALS) algorithms have been proposed. However, they mainly focus on area reduction and are not optimal in reducing the delay of the circuits. In this paper, we propose DALS, a delay-driven ALS framework. DALS works on the AND-inverter graph (AIG) representation of a circuit. It supports a wide range of approximate local changes and some commonly-used error metrics, including error rate and mean error distance. In order to select an optimal set of nodes in the AIG to apply approximate local changes, DALS establishes a critical error network (CEN) from the AIG and formulates a maximum flow problem on the CEN. Our experimental results on a wide range of benchmarks show that DALS produces approximate circuits with significantly reduced delays.