Redundancy of integrated systems has always been used in order to increase vehicle's safety. Some new technologies are however too expensive to be redundant. This paper proposes a new way to preserve the safety by enabling a kind of complementarity between different chassis systems. Optimization-based control allocation redistribution algorithms are used in order to find the best way to combine conflicted systems. Results have shown that one system can take over another one when a failure occurs making the control logic fault-tolerant and reconfigurable. This suits better future cars as additional chassis systems are intended to be integrated.
Modern computerized vehicles offer the possibility of changing vehicle parameters with the aim of creating a novel driving experience, such as an increased feeling of sportiness. For example, electric vehicles can be designed to provide an artificial sound, and the throttle mapping can be adjusted to give drivers the illusion that they are driving a sports vehicle (i.e., without altering the vehicle’s performance envelope). However, a fundamental safety-related question is how drivers perceive and respond to vehicle parameter adjustments. As of today, human-subject research on throttle mapping is unavailable, whereas research on sound enhancement is mostly conducted in listening rooms, which provides no insight into how drivers respond to the auditory cues. This study investigated how perceived sportiness and driving behavior are affected by adjustments in vehicle sound and throttle mapping. Through a within-subject simulator-based experiment, we investigated (1) Modified Throttle Mapping (MTM), (2) Artificial Engine Sound (AES) via a virtually elevated rpm, and (3) MTM and AES combined, relative to (4) a Baseline condition and (5) a Sports car that offered increased engine power. Results showed that, compared to Baseline, AES and MTM-AES increased perceived sportiness and yielded a lower speed variability in curves. Furthermore, MTM and MTM-AES caused higher vehicle acceleration than Baseline during the first second of driving away from a standstill. Mean speed and comfort ratings were unaffected by MTM and AES. The highest sportiness ratings and fastest driving speeds were obtained for the Sports car. In conclusion, the sound enhancement not only increased the perception of sportiness but also improved drivers’ speed control performance, suggesting that sound is used by drivers as functional feedback. The fact that MTM did not affect the mean driving speed indicates that drivers adapted their “gain” to the new throttle mapping and were not susceptible to risk compensation.
Most of automotive researches focus on autonomous vehicles. Studies regarding trajectory planning and trajectory tracking became preponderant. As in case of commercial ground vehicles there is a driver in the loop, one should raise the important question of how the trajectory should be tracked. In this paper, we investigate the influence of controlling integrated chassis systems on the vehicle's behavior. A fixed Model Predictive Control is used to track the trajectory. Tunable vehicle motion control is however used to provide different motion feelings. Results show that a specific trajectory could be followed in different manners. Therefore, vehicle dynamics can be and should be controlled in such a way to generate adaptive trust feelings to passengers in case of autonomous driving.
Vehicle motion control has many challenges to overcome. One of the main problems is robustness against not only environmental changes but also uncertainties about the vehicle itself. This paper focuses on this problem using robust control design at the control architecture's high level. Researches tend to decentralize the control to treat longitudinal and lateral dynamics separately. Here, an overall vehicle model is first proposed and studied to justify the structure that the high-level controller should embrace. Co-simulation results of different combinations showed promising performances to face uncertainties and couplings. Therefore, robust techniques combined with control allocation techniques may enhance autonomous vehicles reliability.
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